As misinformation has become a global phenomenon in the age of digitalization, journalists are expected to provide verified information and prevent the spread of fake news in order to protect the credibility of their profession. This challenge has intensified as conflicts and propaganda became increasingly complex, and in order to stay relevant some may sacrifice reliability for clicks or to serve their own agendas. Therefore, mechanisms that help institutions maintain their reputation and reliability must be implemented. In this study, we propose a solution that enables and automates the verification of sources within the article to enhance the article verification system by enabling the provenance of referenced information. This study helps to lay the groundwork for mechanisms that ensure trust-worthiness, enabling the development of solutions for domains where source-to-source is critical. In doing so, it advances the literature on fake news and blockchain applications offering a tool to help institutions preserve their reputation and elevate standards within the news industry.

This paper introduces a user-friendly tool designed to simplify smart contract analysis through a microservice architecture implemented in Golang and Flutter. The system combines four primary components: a Codec for contract encoding/decoding, an Auditor powered by Slither for security assessment, a Database for version control, and an AI Assistant leveraging gpt-3.5-turbo from OpenAI for automated annotations. User interaction is facilitated through UML-based visual representations that allow intuitive comprehension of the smart contract implementation. An evaluation through the NASA-TLX questionnaire demonstrated that the proposed tool could significantly reduce the overall workload (52.62 versus 71.74) compared to Remix IDE, with notable improvements in mental demand, temporal demand, and frustration levels.

In an era of digital proliferation, especially in energy and AI sectors, this study introduces a blockchain-based framework to enhance data exchange and utilization. This infrastructure, grounded in a decentralized Data Marketplace on a blockchain network, ensures secure and transparent interactions among stakeholders while managing real-world data with a federated architecture compliant with regulations. By integrating connector technology for uniform platform interaction and utilizing smart contracts for autonomous transactions, this solution advances data integrity and intellectual property management through NFTs and datatokens. Here, energy data within the blockchain represents intellectual property managed by multiple parties, symbolized as NFTs and licensed through datatokens (ERC-20 tokens). This approach enhances security, efficiency, and scalability in energy and AI data sharing, showing potential to foster collaboration and innovation.

In the rapidly evolving landscape of blockchain technology, smart contracts stand as pivotal instrument for automating and enforcing digital agreements. However, their creation often necessitates specialized programming skills, hindering broader adoption and accessibility. This paper proposes a pipeline that leverages the capabilities of Large Language Models (LLMs) to automate the generation of smart contracts. By harnessing the natural language understanding and generation capabilities of LLMs, our approach aims to make accessible smart contract development to people that are not familiar with this task. The proposed pipeline employs the CO-STAR methodology to optimize prompt creation for high-quality outputs. Moreover, in order to assess the correctness and reliability of the generated smart contracts, we leverage on Slither, one of the most cutting-edge vulnerability detection tools. Furthermore, we propose a benchmarking suite based on metrics such as compilability, vulnerabilities, and presence of comments, among the others, in order to evaluate the effectiveness of the pipeline in terms of consistency of generated smart contracts, LLM's temperature effect, and prompt selection. The results show that our pipeline is able to produce 98.1% of compilable smart contracts, the temperature value has negligible effect on the generated smart contracts, and the CO-STAR methodology produces valuable and consistent outputs with low-impact vulnerabilities.

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.

The growing number of exploits and hacks on the Ethereum blockchain has led to the development of powerful smart contract vulnerability detection tools and the frequent patching of the smart contract’s programming languages (such as Solidity). At the same time, an ever-increasing number of people are interested in blockchain and smart contract-related topics and willing to build and deploy their own Decentralized Applications (dApp). However, learning a new programming language and its best practices as long as how to actually deploy a smart contract on the blockchain is a difficult task even for experienced developers. Recently, ChatGPT, a new user-friendly deep learning tool, has been released to improve the ability of non-skilled users to write high-quality code and in general, to boost the performances of developers in key tasks related to code writing (i.e., writing functions, explaining runtime errors, fixing bugs, etc.). This paper aims to measure the capabilities of ChatGPT in fixing vulnerable smart contracts and to assess the effectiveness of this tool, determining whether it can be a valuable aid for those who want to correct their own smart contract or want to reuse existing ones by first checking their status and eventually fix their vulnerability. In particular, we asked ChatGPT to fix 143 smart contracts with well-known labeled vulnerabilities. We considered a vulnerability as "fixed" if the code corrected by ChatGPT no longer contained the vulnerability (for this purpose, we exploited Slither, one of the state-of-the-art tools for smart contracts vulnerability detection to check the status of the original and the corrected smart contracts). As a result we obtained that ChatGPT was able to fix bugs and vulnerable smart contracts on average the 57.1% of the time with an increase of +1.4% when a description of the bug was provided in addition to the smart contract’s source code.

A common operation performed in Virtual Reality (VR) environments is locomotion. Although real walking can represent a natural and intuitive way to manage displacements in such environments, its use is generally limited by the size of the area tracked by the VR system (typically, the size of a room) or requires expensive technologies to cover particularly extended settings. A number of approaches have been proposed to enable effective explorations in VR, each characterized by different hardware requirements and costs, and capable to provide different levels of usability and performance. However, the lack of a well-defined methodology for assessing and comparing available approaches makes it difficult to identify, among the various alternatives, the best solutions for selected application domains. To deal with this issue, this article introduces a novel evaluation testbed which, by building on the outcomes of many separate works reported in the literature, aims to support a comprehensive analysis of the considered design space. An experimental protocol for collecting objective and subjective measures is proposed, together with a scoring system able to rank locomotion approaches based on a weighted set of requirements. Testbed usage is illustrated in a use case requesting to select the technique to adopt in a given application scenario.

Virtual character animation is receiving an ever-growing attention by researchers, who proposed already many tools with the aim to improve the effectiveness of the production process. In particular, significant efforts are devoted to create animation systems suited also to non-skilled users, in order to let them benefit from a powerful communication instrument that can improve information sharing in many contexts like product design, education, marketing, etc. Apart from methods based on the traditional Windows-Icons-Menus-Pointer (WIMP) paradigms, solutions devised so far leverage approaches based on motion capture/retargeting (the so-called performance-based approaches), on non-conventional interfaces (voice inputs, sketches, tangible props, etc.), or on natural language processing (NLP) over text descriptions (e.g., to automatically trigger actions from a library). Each approach has its drawbacks, though. Performance-based methods are difficult to use for creating non-ordinary movements (flips, handstands, etc.); natural interfaces are often used for rough posing, but results need to be later refined; automatic techniques still produce poorly realistic animations. To deal with the above limitations, we propose a multimodal animation system that combines performance- and NLP-based methods. The system recognizes natural commands (gestures, voice inputs) issued by the performer, extracts scene data from a text description and creates live animations in which pre-recorded character actions can be blended with performer’s motion to increase naturalness.

Software for computer animation is generally characterized by a steep learning curve, due to the entanglement of both sophisticated techniques and interaction methods required to control 3D geometries. This paper proposes a tool designed to support computer animation production processes by leveraging the affordances offered by articulated tangible user interfaces and motion capture retargeting solutions. To this aim, orientations of an instrumented prop are recorded together with animator's motion in the 3D space and used to quickly pose characters in the virtual environment. High-level functionalities of the animation software are made accessible via a speech interface, thus letting the user control the animation pipeline via voice commands while focusing on his or her hands and body motion. The proposed solution exploits both off-the-shelf hardware components (like the Lego Mindstorms EV3 bricks and the Microsoft Kinect, used for building the tangible device and tracking animator's skeleton) and free open-source software (like the Blender animation tool), thus representing an interesting solution also for beginners approaching the world of digital animation for the first time. Experimental results in different usage scenarios show the benefits offered by the designed interaction strategy with respect to a mouse & keyboard-based interface both for expert and non-expert users.

The players of the digital industry look at network Big Data as an incredible source of revenues, which can allow them to design products, services, and market strategies ever more tailored to users' interests and needs. This is the case of data collected by Web analytics tools, which describe the way users interact with Web contents and where their attention focuses onto during navigation. Given the complexity of information to analyze, existing tools often make use of visualization strategies to represent data aggregated throughout separate sessions and multiple users. In particular, heat maps are often adopted to study the distribution of mouse activity and identify page regions that are more frequently reached during interaction. Unfortunately, since Web contents are accessed via ever more heterogeneous devices, region-based heat maps cannot be exploited anymore to aggregate data concerning user's attention, since the same Web content may move to another page location or exhibit a different aspect depending on the access device used or the user agent setup. This paper presents the design of a visual analytics framework capable to deal with the above limitation by adopting a data collection approach that combines information about regions displayed with information about page structure. This way, the well-known heat map-based visualization can be produced, where interactions can be aggregated on a per-element basis independently of the specific access configuration. Experimental results showed that the framework succeeds in accurately quantifying user's attention and replicating results obtained by manual processing.

The ability to plan a training path fulfilling working needs plays a key role, when it comes to finding the desired job. Nonetheless, in a dynamic scenario characterized by frequent technological innovations and economic changes, as the present one, getting a clear picture of the requirements of the world of work could become difficult, as it would imply performing a deep periodic evaluation of available job offers, and matching them with own previous experience. Intelligent systems able to automatically match résumés with job offers already exist, but they are mostly targeted to recruiters, instead of learners. This work aims at filling this gap, by presenting a system for comparing learners' acquirements - expressed in terms of learning outcomes - with companies requirements. The proposed system relies on semantics for processing natural language texts and exploits bar charts to visualize learning outcomes and their levels in order to quickly depict similarities and differences between résumés and job offers. An evaluation, on 50 volunteers, underlined the added value of the system.

Drone-based delivery of goods could become a reality in the near future, as witnessed by the increasing successful experiences in both research and commercial fields. In this paper, a prototype system exploiting a do it yourself quad copter drone for delivering products is proposed. On the one hand, the hardware choices made in order to limit risks arising from autonomous delivery are presented. On the other hand, a framework for orders placement and shipment is shown. The advantages of a system like the one described in this paper are mainly related to an increased delivery speed, especially in urban contexts with traffic, to the possibility to make deliveries in areas usually difficult to be reached, and to the drone's ability to autonomously carry out consignments. A practical use case, in which the proposed system is used for delivering drugs (an application in which the need to quickly receive the good might be particularly important) is shown. Nevertheless, the proposed prototype could be employed in other contexts, such as take-away deliveries, product shipments, registered mail consignments, etc.

The fast pace of change in high technology product development requires high flexibility and adaptation in product design and technology management. Within this context, appropriate learning environments are planned to shape future Information and Communication Technology staff in such a way they could get able to keep up with new trends and innovations. A constructivist educational model based on Active Learning represents a step towards this vision for staff training. This paper deals with a new organization of a 13 weeks course based on the inverted class model, where students are introduced to the problem-posing domain by a direct involvement of enterprises, which participate specifically to the problem definition and assessment phases. In the case study shown, students simulate a production business unit in all the phases that characterize the product/service development life cycle. A mixed traditional and company-based organization of the course is supposed to improve students competences by proposing solutions leveraging on appropriate tools taken from traditional experiences and also gaining benefits from a learning-by-doing approach that encourages the use of state-of-the-art solutions. A preliminary study is performed to see how expected professional competences are met during the course, with the additional goal of collecting indications that could help to improve the learning framework for future editions of the course.

Students and learners mobility is more and more becoming a praxis. Nonetheless, despite the legislative efforts carried out by the European Commission, devoted to the definition of standards for describing qualifications, the comparison of qualifications contents, for instance in the phases of identification of more suitable training to attend abroad, or during the recognition of prior learning, still requires a considerable amount of efforts and time. This difficulty could be mainly attributed to the fact that qualification contents are usually expressed heterogeneously. In this work, a way to tackle this heterogeneity, by relying on a semantic thesaurus for graphically displaying main similarities and dissimilarities among qualifications is presented. By means of such representation, users could quickly become aware of the main characteristics of a training course, with respect to the others, without having to read all the textual information related to them.

Citizens' mobility and employability are receiving ever more attention by the European legislation. Various instruments have been defined to overcome lexical and semantic differences in the descriptions of qualifications, résumés and job profiles. However, the above differences still represent a significant constraint when abilities of non-European people have to be validated either for education and training or occupation purposes. In this work, a web platform that exploits semantic technologies to address such heterogeneity issues is presented. The platform allows migrants to annotate their knowledge, skills and competences in a shared format based on the European tools. The resulting knowledge base is then used to enable the automatic matchmaking of job seekers' abilities with companies' needs. The platform can additionally be used to support students and workers in the identification of their competence gap with respect to a given education or occupation opportunity, so that to personalize their further training.