The recent need of supporting the diffusion of electric mobility around the world to progressively substitute petrol transport means, leads to the development of new hardware and software technologies to make even more convenient the use of electric vehicles (EVs). High purchasing costs and long recharging times are two major factors slowing this transition. In addition, from the end users perspective, using EV in long distance journeys is still not convenient despite the increasing diffusion of fast charging infrastructures. In this context, to facilitate traveling with EVs in long distance trips, this paper proposes a trip planner prototype based on Deep Reinforcement Learning (DRL). The trip planner prototype has the goal to suggest to the drivers the best charge stops to be performed during the trip according to the user needs and preferences. Charging stops are optimized, using the available Charging Points (CPs) along the route from origin to destination, and are shown to the user on a map taking into account important information like the EV State of Charge (SoC), the cruise velocity, and the presence of point of interest (e.g. restaurant, hotel, shops, etc.) close around. The trip plan can be done according to three objectives: minimizing the travel time, minimizing the charging costs, optimizing travel time and cost. The proposed DRL approach is compared against Genetic Algorithm (GA), heuristic, and optimization approaches considering a real-world EV trip.

The rise of the Internet of Things has introduced new challenges related to data security and transparency, especially in industries like agri-food where traceability is critical. Traditional cloud-based solutions, while scalable, pose security and privacy risks. This paper proposes a decentralized architecture using Blockchain technology to address these challenges. We deploy IoT sensors connected to a Raspberry Pi for edge processing and utilize Hyperledger Fabric, a private Blockchain, to manage and store data securely. Two approaches are evaluated: computation of a Discomfort Index on the Raspberry Pi (edge processing) versus performing the same computation on-chain using smart contracts. Performance metrics, including latency, throughput, and error rate, are measured using Hyperledger Caliper. The results show that edge processing offers superior performance in terms of latency and throughput, while Blockchain-based computation ensures greater transparency and trust. This study highlights the potential of Blockchain as a viable alternative to centralized cloud systems in IoT environments and suggests future research in scalability, hybrid architectures, and energy efficiency.

This paper explores the integration of generative AI and large language models into the realm of software engineering education and training, with a specific focus on the transformation of traditional peer assessment methodologies. The motivation stems from the growing demand for innovative educational techniques that can effectively engage and empower learners in mastering Software Engineering principles. The proposed approach involves presenting students with modeling exercises solved by ChatGPT, prompting them to critically evaluate and provide constructive feedback on the generated solutions. By engaging students in a dialogue with the AI model, we aim to foster a dynamic learning environment where learners can articulate their considerations and insights, thereby enhancing their comprehension of software engineering principles, critical thinking and self evaluation skills. Preliminary results from pilot implementations indicate promising outcomes, suggesting that this approach not only enhances the quality of peer feedback but also contributes to a more interactive and engaging educational experience.

The development of Quantum Computing has brought great advantages in terms of computational power that can be seen as an opportunity or as a potential threat to currently implemented systems. The security of a platform can be easily broken if its founding algorithms are not quantum-safe. For this reason, it is crucial to understand how quantum computers work and how much time is needed to switch to quantum-safe platforms. The main contribution of this paper consists of a software architecture for modular Blockchains to let multiple encryption algorithms coexist in order to mine new quantum-safe blocks without discarding old, validated, quantum-broken ones. Old blocks will still be unsafe for post-quantum cryptography, but this is not a threat to the chain integrity.

Traceability is a useful tool for consumers to gather as much information as possible about a particular product.Businesses, on the other hand, see traceability as a strategic marketing tool because it allows them to ensure the quality of their goods to customers in a transparent manner.The ability to readily access all information about an agri-food product is critical to customer trust.Products' information can include where they were manufactured, where they came from, what steps they took to reach at the shelter, and so on.The Blockchain technology is an illustration of how all industries are shifting toward technology and communication.The aim of this paper is to present the Tracecoop project and give an overview of the architecture of the proposed system.The platform ensures trust and guarantees a sense of community both for the consumer and the producer.

Fog computing paradigm is emerging after a decade's dominance of cloud‐based system design and architecture. Now, instead of centralising the computation and coordination to remote services, these are deployed and distributed to all over physical surroundings and network nodes, including cloud services, smart gateways, and network edge devices. At the moment, the majority of the Internet of things (IoT) systems and software has built on top of open Web‐based technologies. The authors assume that with the ever‐growing number and heterogeneity of connected devices, it becomes ever‐more crucial to have open standards that support interoperability and enable interactions. They review the current technological space for architecting Web technology‐based IoT software in the coming era of fog computing. They focus on fundamental research challenges and discuss the emerging issues.

The main aim of data mining techniques and tools is that of identify and extract, from a set of (big) data, implicit patterns which can describe static or dynamic phenomena. Among these latter business processes are gaining more and more attention due to their crucial role in modern organizations and enterprises. Being able to identify and model processes inside organizations is for sure a key asset to discover their weak and strong points thus helping them in the improvement of their competitiveness. In this paper we describe a prototype system able to discover business processes from an event log and classify them with a suitable level of abstraction with reference to a related business ontology. The identified process, and its corresponding level of abstraction, depends on the knowledge encoded in the reference ontology which is dynamically exploited at runtime. The tool has been validated by considering examples and case studies from the literature on process mining.

Due to the on-demand and dynamic nature of Cloud, there is an increasing interest for automated management of adaptation and (possibly) re-deployment of cloud applications to realize quality requirements and evolution needs autonomously at run-time. This paper proposes a fast and automated approach for adapting and redeploying a cloud application at run-time as dictated by evolution needs and sudden changes in the operating environment conditions. The proposed approach exploits a graph-based model and an algorithm that extracts a sub-graph identifying the adaptation processes to be executed according to evolution changes. The approach is general enough to be implemented by any cloud application management framework. A TOSCA-based description of the structure and management aspects of the cloud application may be updated according to the above mentioned sub-graph. Then, this description may be processed by a TOSCA-compliant runtime environment to effectively adapt and possibly re-deploy the cloud application in an automated manner. The paper also illustrates the instantiation of this generic approach for adapting an e-commerce cloud application.

Self-adaptive systems are modern applications in which the running software system should be able to react on its own, by dynamically adapting its behavior, for sustaining a required set of qualities of service, and dynamic changes in the context or in user requirements. They are typically involved in Future Internet development such as the Internet of Things where interoperability, flexibility, and adaptability are key requirements. Convergence of contents, services, things and networks seems to be the cornerstone to fullfil these requirements. We propose a reflective approach to provide a common abstraction for automating the deployment of component based applications in the Internet of Things environment. The proposed framework allows the design of heterogeneous, distributed, and adaptive applications built on the component based software engineering paradigm. The framework considers a metamodel instantiated in a Rest middleware properly modified for allowing different implementations by using reflective design patterns. We are currently working to refine the framework metamodel and to validate it in several implementation domains.

Recent advances in information and communication technologies have made multimedia-on-demand services technically and economically feasible. Important aspects of such systems are the resource sharing techniques, which allow the simultaneous service of a large number of users with considerable savings in terms of network bandwidth and server resources. In this paper, we report the results of a study which analyzes batching and buffering techniques, which involves serving all video requests issued during a short interval of time with a single stream. The mathematical model, based on queueing networks, allows the evaluation of the main system performance (average and probability distribution of the number of streams, percentage reduction of resources, and so on) as a function of load and batching interval duration. Simulation experiments confirm the analytical model in the whole range of considered conditions.

In this paper we present a Description Logic approach to extended matchmaking between Demands and Supplies in an Electronic Marketplace, which allows the semantic-based treatment of negotiable and strict requirements in the description.To this aim we exploit two novel non-standard Description Logic inference services, Concept Contraction -which extends satisfiability-and Concept Abduction -which extends subsumption.Based on these services we devise algorithms to find negotiation spaces and to determine the quality of a possible match, also in the presence of a distinction between strictly required and optional elements.

More and more resources are becoming available on the Web, and there is a growing need for infrastructures that, based on advertised descriptions, are able to semantically match demands with supplies.We formalize general properties a matchmaker should have, then we present a matchmaking facilitator, compliant with desired properties.The system embeds a NeoClassic reasoner, whose structural subsumption algorithm has been modified to allow match categorization into potential and partial, and ranking of matches within categories. Experiments carried out show the good correspondence between users and system rankings.

In this paper we propose a formal method for web applications verification. The verification process is carried out by checking that either the system always satisfies a model of the specifications or by producing a counter-example.We represent the system as a Kripke structure and model a web site as a graph. Model checking is reformulated as checking that each initial state satisfies the specifications. We adopt Computation Tree Logic (CTL) as language to define the properties to be verified.The proposed formal method has been deployed in AnWeb, a tool for automatic support in the design of web applications. The tool provides an interface to the SMV model checker. The system parses the HTML source code of web pages, including code for dynamic pages, builds the model in SMV input language and provides the proper CTL specifications to the SMV tool.

Web engines crawl hyperlinks to search for new documents; yet when they index discovered documents they basically revert to conventional information retrieval models and concentrate on the indexing of terms in a single document. We propose to overcome such limits with an approach based on temporal logic. By modeling a web site as a finite state transition system we are able to define complex and selective queries over hyperlinks with the aid of Computation Tree Logic operators. We deployed the proposed approach in a prototype system that allows users pose queries in natural language. Queries are automatically translated in Computation Tree Logic, and the answer returned by our system is a set of paths. Experiments carried out with the aid of human experts show improved retrieval effectiveness with respect to current search engines.

We propose a structured approach to the problem of retrieval of images by content and present a description logic that has been devised for the semantic indexing and retrieval of images containing complex objects. As other approaches do, we start from low-level features extracted with image analysis to detect and characterize regions in an image. However, in contrast with feature-based approaches, we provide a syntax to describe segmented regions as basic objects and complex objects as compositions of basic ones. Then we introduce a companion extensional semantics for defining reasoning services, such as retrieval, classification, and subsumption. These services can be used for both exact and approximate matching, using similarity measures. Using our logical approach as a formal specification, we implemented a complete client-server image retrieval system, which allows a user to pose both queries by sketch and queries by example. A set of experiments has been carried out on a testbed of images to assess the retrieval capabilities of the system in comparison with expert users ranking. Results are presented adopting a well-established measure of quality borrowed from textual information retrieval.