Decentralised energy ecosystems suffer from data-governance, scalability and adoption barriers. Although blockchain-based marketplaces can offer transparency and security in Local Energy Communities (LECs), most existing solutions struggle with rigid token models, limited performance, and steep usability barriers. Building on a previous framework, this study presents an enhanced marketplace that integrates a modular blockchain layer, custodial identity management, and a dual-token model for flexible licensing and pricing. Testing on a per-missioned Quorum network with asynchronous queueing demonstrated notable improvements in transaction throughput and user responsiveness under load, while the custodial onboarding flow simplified access for non-technical participants. A refined policy enforcement mechanism further aligns the system with emerging federation standards, mitigating earlier shortcomings related to performance, data sovereignty, and scalability. Benchmarking on a five-node Quorum Proof of Authority (PoA) deployment (one RPC node and four validator nodes) showed that all key operations, including license issuance and asset usage, consistently completed in under 8 seconds, confirming the system’s suitability for possible energy data applications. Planned extensions include cross-domain interoperability, self-service governance tools, and zero-knowledge proofs, underscoring this architecture’s potential as a robust, future-ready platform for federated energy data ecosystems.

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.

Summary The idea of hash time‐lock contracts (HTLCs) has been around from 2013. Nowadays these contracts power the majority of atomic swaps making decentralized exchange of tokens possible. On the other hand, HTLCs also have some flaws. For example they can only be instantiated between two parties. This is highly inefficient when many participants want to exchange tokens between the same pair of blockchains at the same time, because the number of transactions increases linearly in the number of participants. To solve this problem, in this article, we present MP‐HTLC. MP‐HTLC lets multiple users exchange tokens on different blockchains in a single instantiation of the protocol without any leader election. We prove that in case of a UTXO‐based blockchain the number of transactions remains constant regardless the number of participants. We are able to maintain the security assumptions of HTLCs using multiparty computation in the creation of the secret preimage and threshold signatures to manage transaction signing. We also present an implementation for each of the aspects of the protocol.

First paragraph: Blockchain technologies make agreement among untrusted parties possible, without the need for certification authorities. Proposed frameworks have been put forward in sector as diverse as finance, health care, notary, intellectual property management, identity, provenance, international cooperation, social good, and security to cite but a few. Smart contracts, that is, self‐enforcing agreements in terms of executable software running on blockchains, have been developed in several contexts. Such an under‐definition computational model introduces innovative aspects, such as the economics and trust of the decentralized computation relying on the shared contribution of peers and their decentralized consensus.

People with autism have idiosyncratic sensory experiences, which may impact on how they live the “spaces” of their everyday life. Starting from an investigation of their conception and experience of “secure places,” we defined a series of user requirements for designing technology that supports their everyday movements in the urban environment. On the basis of such requirements, we developed an interactive system that leverages crowdsourcing mechanisms to map places that are perceived as secure by the population with autism.

In recent years, new digital technologies are being used to support the navigation and the analysis of scientific publications, justified by the increasing number of articles published every year. For this reason, experts make use of on-line systems to browse thousands of articles in search of relevant information. In this paper, we present a new method that automatically assigns meanings to references on the basis of the citation text through a Natural Language Processing pipeline and a slightly-supervised clustering process. The resulting network of semantically-linked articles allows an informed exploration of the research panorama through semantic paths. The proposed approach has been validated using the ACL Anthology Dataset containing several thousands of papers related to the Computational Linguistics field. A manual evaluation on the extracted citation meanings carried to very high levels of accuracy. Finally, a freely-available web-based application has been developed and published on-line.

This paper describes the preliminary results of a project aimed to support people with autism in finding city places that match their "sensorial" preferences and aversions. Through a participatory design approach, we designed an interactive map that collects sensorial data about the urban environment exploiting crowdsourcing mechanisms.

Scaling and visualization theories about traditional maps can be extended to digital maps in order to develop web applications using maps as information management systems, data viewer and recommender systems. Starting from current limitations of web application using maps, we outline an alternative approach based on building an indexing system connecting scales and contents, on fixing visualization and styling rules, and attributing an active role to the user in defining goals, area of interest and related contents. This contribution presents the first steps towards a theoretical framework to develop map-based applications where contents are connected to the map entities and the user interactions impact on the status of the map and of the application at the same time.

Nowadays, many tools and systems are available to allow the analysis and the comparison of researchers' scientific production. The reason underlying such interest is evident: promotions, funding allocations, and employments are currently based on the evaluation (and direct comparisons) of publication lists. Existing measures, like H-index, aim at supporting this process by automatic calculations of quality and/or quantity indices. In this work, we propose a demonstration of a web environment, available at http://d-index.di.unito.it, that faces the problem of studying the impact of collaborations in research communities. The presented system allows the estimation of the impact of each scientific collaboration on the production of each researchers indexed by the DBLP bibliographic database by means of a novel time-based modeling of collaborative environments. The proposed application provides several interactions and visualization schemes to deeply discover clear and latent insights, over time, around the work of each researcher. Furthermore, it allows cross-community rankings of the authors depending on similar collaboration patterns and dependences.

Twitter is a popular microblogging service that acts as a ground-level information news flashes portal where people with different background, age, and social condition provide information about what is happening in front of their eyes. This characteristic makes Twitter probably the fastest information service in the world. In this article, we recognize this role of Twitter and propose a novel, user-aware topic detection technique that permits to retrieve, in real time, the most emerging topics of discussion expressed by the community within the interests of specific users. First, we analyze the topology of Twitter looking at how the information spreads over the network, taking into account the authority/influence of each active user. Then, we make use of a novel term aging model to compute the burstiness of each term, and provide a graph-based method to retrieve the minimal set of terms that can represent the corresponding topic. Finally, since any user can have topic preferences inferable from the shared content, we leverage such knowledge to highlight the most emerging topics within her foci of interest. As evaluation we then provide several experiments together with a user study proving the validity and reliability of the proposed approach.

Tensors (multidimensional arrays) are widely used for representing high-order dimensional data, in applications ranging from social networks, sensor data, and Internet traffic. Multiway data analysis techniques, in particular tensor decompositions, allow extraction of hidden correlations among multiway data and thus are key components of many data analysis frameworks. Intuitively, these algorithms can be thought of as multiway clustering schemes, which consider multiple facets of the data in identifying clusters, their weights, and contributions of each data element. Unfortunately, algorithms for fitting multiway models are, in general, iterative and very time consuming. In this article, we observe that, in many applications, there is a priori background knowledge (or metadata) about one or more domain dimensions. This metadata is often in the form of a hierarchy that clusters the elements of a given data facet (or mode). We investigate whether such single-mode data hierarchies can be used to boost the efficiency of tensor decomposition process, without significant impact on the final decomposition quality. We consider each domain hierarchy as a guide to help provide higher- or lower-resolution views of the data in the tensor on demand and we rely on these metadata-induced multiresolution tensor representations to develop a multiresolution approach to tensor decomposition. In this article, we focus on an alternating least squares (ALS)--based implementation of the two most important decomposition models such as the PARAllel FACtors (PARAFAC, which decomposes a tensor into a diagonal tensor and a set of factor matrices) and the Tucker (which produces as result a core tensor and a set of dimension-subspaces matrices). Experiment results show that, when the available metadata is used as a rough guide, the proposed multiresolution method helps fit both PARAFAC and Tucker models with consistent (under different parameters settings) savings in execution time and memory consumption, while preserving the quality of the decomposition.

In this work, we demonstrate a web application, available at http://d-index.di.unito.it, that permits to analyze the scientific profiles of all the researchers indexed by DBLP by focusing on the collaborations that contributed to define their curricula. The presented application allows the user to analyze the profile of a researcher, her dependence degrees on all the co-authors (along her entire scientific publication history) and to make comparisons among them in terms of dependence patterns. In particular, it is possible to estimate and visualize how much a researcher has benefited from collaboration with another researcher as well as the communities in which she has been involved. Moreover, the application permits to compare, in a single chart, each researcher with all the scientists indexed in DBLP by focusing on their dependences with respect to many other parameters like the total number of papers, the number of collaborations and the length of the scientific careers.

Tensors (multi-dimensional arrays) are widely used for representing high-order dimensional data, in applications ranging from social networks, sensor data, and Internet traffic. Multi-way data analysis techniques, in particular tensor decompositions, allow extraction of hidden correlations among multi-way data and thus are key components of many data analysis frameworks. Intuitively, these algorithms can be thought of as multi-way clustering schemes, which consider multiple facets of the data in identifying clusters, their weights, and contributions of each data element. Unfortunately, algorithms for fitting multi-way models are, in general, iterative and very time consuming. In this paper, we observe that, in many applications, there is a priori background knowledge (or metadata) about one or more domain dimensions. This metadata is often in the form of a hierarchy that clusters the elements of a given data facet (or mode). In this paper, we investigate whether such single-mode data hierarchies can be used to boost the efficiency of tensor decomposition process, without significant impact on the final decomposition quality. We consider each domain hierarchy as a guide to help provide higher- or lower-resolution views of the data in the tensor on demand and we rely on these metadata-induced multi-resolution tensor representations to develop a multiresolution approach to tensor decomposition. In this paper, we focus on an alternating least squares (ALS) based implementation of the PARAllel FACtors (PARAFAC) decomposition (which decomposes a tensor into a diagonal tensor and a set of factor matrices). Experiment results show that, when the available metadata is used as a rough guide, the proposed multiresolution method helps fit PARAFAC models with consistent (for both dense and sparse tensor representations, under different parameters settings) savings in execution time and memory consumption, while preserving the quality of the decomposition.

Most of the existing document and web search engines rely on keyword-based queries. To find matches, these queries are processed using retrieval algorithms that rely on word frequencies, topic recentness, document authority, and (in some cases) available ontologies. In this paper, we propose an innovative approach to exploring text collections using a novel keywords-by-concepts (KbC) graph, which supports navigation using domain-specific concepts as well as keywords that are characterizing the text corpus. The KbC graph is a weighted graph, created by tightly integrating keywords extracted from documents and concepts obtained from domain taxonomies. Documents in the corpus are associated to the nodes of the graph based on evidence supporting contextual relevance; thus, the KbC graph supports contextually informed access to these documents. In this paper, we also present CoSeNa (Context-based Search and Navigation) system that leverages the KbC model as the basis for document exploration and retrieval as well as contextually-informed media integration.