Abstract The rapid adoption of blockchain technology highlighted the importance of ensuring the security of smart contracts due to their critical role in automated business logic execution on blockchain platforms. This paper provides an empirical evaluation of automated vulnerability analysis tools specifically designed for Solidity smart contracts. Leveraging the extensive SmartBugs 2.0 framework, which includes 20 analysis tools, we conducted a comprehensive assessment using an annotated dataset of 2,182 instances, manually labeled at the line level with vulnerability labels. Our evaluation highlights the detection effectiveness of these tools in detecting various types of vulnerabilities, as categorized by the DASP TOP 10 taxonomy. We evaluated the efficacy of a Large Language Model-based detection method on two popular datasets. In this case, we obtained inconsistent results with the two datasets, showing unreliable detection when analyzing real-world smart contracts. Our study identifies significant variations in the accuracy and reliability of different tools and demonstrates the advantages of combining multiple detection methods to improve vulnerability identification. We identified a set of 3 tools that, combined, achieve up to 76.78% found vulnerabilities, taking less than one minute to run, on average. This study contributes to the field by releasing the largest dataset of manually analyzed smart contracts with line-level vulnerability annotations and by conducting the largest empirical evaluation of tools to date.

Abstract In this paper, we investigate the strategies adopted by Solidity developers to fix security vulnerabilities in smart contracts. Vulnerabilities are categorized using the DASP TOP 10 taxonomy, and fixing strategies are extracted from 364 commits collected from open-source Solidity projects on GitHub. Each commit was selected through a two-phase process: an initial filter using natural language processing techniques, followed by manual validation. We assessed whether these fixes adhere to established academic guidelines. Our analysis shows that 60.55% of the commits aligned with at least one literature-based recommendation, particularly for well-documented vulnerability types such as Reentrancy and Arithmetic. However, adherence dropped significantly for categories like Denial of Service, Time Manipulation, and Bad Randomness, highlighting gaps between academic best practices and real-world developer behavior. From the remaining 143 non-aligned commits, we identified 27 novel fixing strategies not previously discussed in the literature. To evaluate their quality, we conducted a structured questionnaire involving 9 experts from both academia and industry. Their feedback indicated high perceived effectiveness of the new fixes, especially for vulnerabilities like Reentrancy and Unchecked Return Values. Generalizability received more varied responses, suggesting context-specific applicability. Finally, we performed a post-fix evolution analysis on over 6700 subsequent commits to assess the long-term stability of the fixes. Most patches remained unchanged, confirming their persistence in production code. Our findings offer practical insights into how vulnerabilities are fixed in smart contracts today, reveal promising emerging patterns, and help bridge the gap between academic guidelines and developer practices.

Abstract Purpose Recent advances in AI, especially in large language models, have created new opportunities to integrate human and artificial agents through shared linguistic capabilities. This paper presents a multi-agent organizational framework in which human agents, LLMs, and specialized agents (narrow AIs) collaborate via dynamic, topic-based group formation. Topic-driven interactions enable agents to coalesce around evolving interests, supported by threshold-based protocols for temporal adaptation, topic emergence, and participation. Methods Within our framework, human agents guide the overall system objectives, while consultant agents (LLMs) provide semantic analysis and mediation, and specialized agents perform focused domain tasks. By leveraging automated topic modeling, the approach eschews rigid ontologies and instead supports adaptive and interpretable content management. Mathematical properties ensure system coherence—across roles, tasks, and timescales—while allowing the natural evolution of interests and groups. Results We illustrate the framework’s versatility with example scenarios in emergency response, healthcare research, and financial decision-making, emphasizing how human decision-makers, LLM-based consultants, and specialized worker agents jointly fulfill complex goals through transparent topic alignment and threshold-driven coordination. This formalization advances human-computer interaction as a multi-agent phenomenon that integrates human insight with the strengths of next-generation AI models in a cohesive, evolving system.

Blockchain-based Information Ecosystems (BBIEs) are a type of information ecosystem in which blockchain technology is used to provide a trust mechanism among parties and to manage shared business logic, breaking the traditional scheme of Information Ecosystems dominated by a leading company and leveraging the decentralization of data management, information flow, and business logic. In this paper, we propose architecture and technical aspects concerning creating a BBIE, underlining the advantages supplied and the logic decomposition among the business and storage components. The requirements are derived from the current needs of the collaborative business and the data collected by surveying practitioners. To get these needs we followed the Grounded Theory research approach. We validate our architectural schema against a case study on managing a wine supply chain–involving different companies and supervision authorities. The proposed solution integrates blockchain-based applications with the existing information system as a module of the ecosystem, leveraging on the low costs, scalability, and high-level security because of the restricted access to the network. We must go a long way in deepening and refining the possibilities offered by technology in supporting innovative multi-organizational business models. BBIEs can contribute substantially to paving the way in such a direction.

Smart contracts show a high potential to make supply chain management strategies epochally leap towards higher levels of productivity, not only in the functioning of production processes but also in terms of product innovation and overall economic returns. This article illustrates the principle of Income Sharing as a highly performing economic strategy for supply chains with a natural implementation in blockchain smart contracts. It proposes a blockchain-based architecture that uses smart contracts to implement various algorithmic versions of the Income Sharing principle among companies participating in a supply chain. The formation of the total income and its consequent redistribution are calculated taking into account the role of the technological platform automating these procedures, which therefore becomes a party to the inter-company business project of a supply chain in the alternative roles, as feasible in business practice, of Blockchain-as-a-Service and Blockchain-as-a-Partner. The approach is implemented on Hyperledger Fabric, the most widespread platform for private and consortium blockchains. We compare and justify this design choice with the alternative given by public blockchains, with specific attention to Ethereum.

A key component of blockchain technology is the ledger, viz., a database that, unlike standard databases, keeps in memory the complete history of past transactions as in a notarial archive for the benefit of any future test. In second-generation blockchains such as Ethereum, the ledger is coupled with smart contracts, which enable the automation of transactions associated with agreements between the parties of a financial or commercial nature. The coupling of smart contracts and ledgers provides the technological background for very innovative application areas, such as Decentralized Autonomous Organizations (DAOs), Initial Coin Offerings (ICOs), and Decentralized Finance (DeFi), which propelled blockchains beyond cryptocurrencies that were the only focus of first generation blockchains, such as Bitcoin. However, the currently used implementation of smart contracts as arbitrary programming constructs has made them susceptible to dangerous bugs that can be exploited maliciously and has moved their semantics away from that of legal contracts. We propose here to recompose the split and recover the reliability of databases by formalizing a notion of contract modeled as a finite-state automaton with well-defined computational characteristics derived from encoding in terms of allocations of resources to actors, as an alternative to the approach based on programming. To complete the work, we use temporal logic as the basis for an abstract query language that is effectively suited to the historical nature of the information kept in the ledger.

Private distributed ledgers (DLs) appear to be more successful than public blockchains at supporting innovative business ecosystems in the form of enterprise consortia. In fact, by untying transactions from the native cryptocurrencies of public blockchains, consortium DLs can smoothly interface with the various sectors of the traditional economy with the capability to innovate it. Furthermore, eliminating mining greatly increases the computational efficiency of these technologies compared to public blockchains. So far, consortium DLs have been used primarily for traceability and document management. Now the time is ripe to move forward, impacting business and profitability. We describe here a consortium model based on distributed ledger technology that applies the old principle that “unity is strength” by deploying the economic method of Revenue Sharing as a smart contract. We illustrate the model through a prototype implementation on Hyperledger Fabric, the most widely adopted among the platforms for consortium distributed ledgers.

Abstract Blockchain is a decentralized transaction and data management solution, the technological leap behind the success of Bitcoin and other cryptocurrencies. As the variety of existing blockchains and distributed ledgers continues to increase, adopters should focus on selecting the solution that best fits their needs and the requirements of their decentralized applications, rather than developing yet another blockchain from scratch. In this article we present a conceptual framework to aid software architects, developers, and decision makers to adopt the right blockchain technology. The framework exposes the interrelation between technological decisions and architectural features, capturing the knowledge from existing academic literature, industrial products, technical forums/blogs, and experts' feedback. We empirically show the applicability of our framework by dissecting the platforms behind Bitcoin and other top 10 cryptocurrencies, aided by a focus group with researchers and industry practitioners. Then, we leverage the framework together with key notions of the architectural tradeoff analysis method to analyze four real‐world blockchain case studies from industry and academia. Results shown that applying our framework leads to a deeper understanding of the architectural tradeoffs, allowing to assess technologies more objectively and select the one that best fit developers' needs, ultimately cutting costs, reducing time‐to‐market and accelerating return on investment.

In this paper we face some relevant issues on the relations between Web communities and ontologies. We build an operator that constructs a weak Web community, according to the definition given in,starting from a seed of Web sites. The necessity of such an operator is derived from a problem arisen in the model developed in, in which some relevant concepts in automotive oriented ontology were not given a corresponding Web community. This fact -if not considered- can bring automatic ontology development to some non-correct results. In this work we define and analyze a new operator, called Com, with the tools furnished by the method of parametrization and we find that, given a seed S and the induced graph I(S), the community generated by our operator is monotonic with respect to clustering and is denser than the original graph I(S).

Techniques that optimize computations by reusing partial results have a long tradition in computer science. Seen from the point of view of sequential computations, all these techniques share the common execution strategy of storing partial results in a centralized data structure, while they differ as to how the results are computed, e.g. in a data-driven or constraint-driven fashion. Concurrent systems, namely the wide variety of systems that range from fine-grained parallelism to coarse-grained distribution, add another variable into the game. In fact, information reuse is here tangled with issues of local memory of agents and inter-agent communication. Thus, optimal strategies for information reuse directly affect agent configurations as well as agent communication protocols and strictly depend on those morphological aspects of the computational domain related to the sharing of structures among different data values. In this paper, we define a formal framework suitable for the study of information reuse from the point of view of concurrent systems. The main result of our work is in the identification of two distinct morphological features of computational domains, namely recursively replicated structures and structure copying. These features induce two different forms of information reuse that can be optimized, respectively, by solipsistic agents with large local memory and by large bandwidth networks of collaborative agents.

Summary form only given. The amount of information available from electronic sources on the World Wide Web and other on-line information repositories is highly heterogeneous and increasing dramatically. Tools are needed to extract relevant information from these repositories. The Constraint-Based Knowledge Brokers project (CBKB) at RXRC Grenoble realizes sophisticated facilities for efficient information retrieval, schema integration, and knowledge fusion. The current implementation of the CBKB research prototype involves three kinds of agents: a) users, who input queries and process answers (i.e., ranking, fusion) through a GUI; b) wrappers, capable of interrogating heterogeneous information sources, which can provide answers to elementary queries (essentially various public bibliographic catalogues available on the Web, as well as preprint archives and opera information repositories); c) brokers, which can manage complex queries (i.e., decompose a complex query, recompose the partial answers, synthesize a full answer) and which mediate between the GUI and the different wrappers. The core of the system is given by the brokers, which provide various important services such as intelligent caching, filtering and knowledge combination. Requests, intermediate information, and results are internally represented via feature constraints. Requests do not need to be fully defined; they may correspond to partial specifications of the requested information.

No abstract available.

The promise of workflow solutions for coordinating organizational processes is currently being obscured by strong criticism of the rigidity of their work representations.This rigidity arises in part from viewing work processes as unfolding along a single line of temporally chained activities.In reality, work evolves both horizontally, in the cooperation of causally unrelated, but information-sharing tasks, and vertically, in the coordination of causally-dependent activities.In this paper, we present our process modeling approach which (1) views documents and tasks as duals of each other, capturing horizontal cooperation and (2) exploits constraints to express the SOP dependencies among related activities and documents within the framework of generative rule-based grammars for processes.thus handling vertical coordination.

Ve introduce an abstract form of interobject communication for object-oriented concurrent programming based on the proof theory of Linear Logic, a logic introduced to provide a theoretical basis for the study of concurrency.Such a form of communication, which we call forumbased communication, can be seen as a refinement of blackboard-based communication in terms of a more local notion of resource consumption.Forumbased communication is introduced as part of a new computational model for the object-oriented concurrent programming language LO, presented at last year OOPSLA/ECOOP (1990), which exploits the proof-theory of Linear Logic also to achieve a powerful form of knowledge-sharing.

We introduce a first-order version of Categorial Grammar, based on the idea of encoding syntactic types as definite clauses.Thus, we drop all explicit requirements of adjacency between combinable constituents, and we capture wordorder constraints simply by allowing subformulae of complex types to share variables ranging over string positions.We are in this way able to account for constructiods involving discontinuous constituents.Such constructions axe difficult to handle in the more traditional version of Categorial Grammar, which is based on propositional types and on the requirement of strict string adjacency between combinable constituents.We show then how, for this formalism, parsing can be efficiently implemented as theorem proving.Our approach to encoding types:as definite clauses presupposes a modification of standard Horn logic syntax to allow internal implications in definite clauses.This modification is needed to account for the types of higher-order functions and, as a consequence, standard Prolog-like Horn logic theorem proving is not powerful enough.We tackle this

There has recently been a revival of interest in Categorial Grammars (CG) among computational linguists. The various versions noted below which extend pure CG by including operations such as functional composition have been claimed to offer simple and uniform accounts of a wide range of natural language (NL) constructions involving bounded and unbounded "movement" and coordination "reduction" in a number of languages. Such grammars have obvious advantages for computational applications, provided that they can be parsed efficiently. However, many of the proposed extensions engender proliferating semantically equivalent surface syntactic analyses. These "spurious analyses" have been claimed to compromise their efficient parseability.The present paper describes a simple parsing algorithm for our own "combinatory" extension of CG. This algorithm offers a uniform treatment for "spurious" syntactic ambiguities and the "genuine" structural ambiguities which any processor must cope with, by exploiting the associativity of functional composition and the procedural neutrality of the combinatory rules of grammar in a bottom-up, left-to-right parser which delivers all semantically distinct analyses via a novel unification-based extension of chart-parsing.