Blockchain Papers

Ning Ge, Jinwen Yang, Tianyu Yu, Wei Liu

A smart legal contract is a legally binding contract in which some or all of the contractual obligations are defined and performed automatically by a computer program. As its software requirement, the legal contract is composed of legal clauses expressing the execution logic and time constraints between events in natural language. When formally verifying a smart legal contract to ensure the requirements’ conformance, it is necessary to translate the time-constrained functional requirements (TFRs) into property specifications like Metric temporal logic (MTL) as the input of a model checker. Instead of costly and error-prone manual writing, this work automates the TFR detection and the specification generation using deep learning, named AutoMTL-Spec. We separate the MTL specification generation approach into four tasks: TFR detection, intermediate representation structure extraction, event sequence/time point extraction, and MTL generation, respectively. We construct a dataset including 43 contracts of four categories, 4608 terms, and 277 TFRs. The experimental results showed that all three models significantly outperform the baselines. Most of the indicators of the three learning tasks reached near to or more than 90%.

Ogonna Annette Onwudiegwu

Non-Fungible Tokens (NFTs) have gained significant popularity as a means of ownership and authentication within the metaverse, a virtual reality space where users interact with each other using avatars.In fact, it has been considered to be the legal tender of the metaverse.As the Metaverse and NFTs continue to revolutionize various industries, their impact on Intellectual Property (IP) rights has raised critical concern especially regarding its effect on IP lawyers.It is against this backdrop that this paper delves into the intersection of NFTs and the metaverse by examining the implications, prospects and challenges faced by IP lawyers in an increasingly digitalized world.It also aims to provide valuable insights into the evolving landscape of IP law in relation to NFTs and the metaverse.

Andreas Langenohl

Decentralized finance (DeFi) shares with blockchain technologies a refutation of interpersonal trust and norm-obedient behavior, suggesting technical solutions to problems of financial coordination that substitute for that lack. Through this, DeFi equips the technology with the capacity to induce norm-obedient behavior. The chapter discusses the ethical implications of this strategy against the background of an understanding of ethics, borrowed from Michel Foucault, as differing from moral code and norm-obedient behavior. From that perspective, ethics is first and foremost a reflection on moral self-conduct at the forefront of (moral or technical) code. In the case of Defi, ethical behavior is problematized and debated that concerns itself with the subjective qualities that must be cultivated in order to participate in DeFi in the first place, to engage in it with one’s resources and to accept its incentive structures, rules and regulations.

Aidin Rasti, Amal Ahmed Anda, Sofana Alfuhaid, Alireza Parvizimosaed · 8 authors

Complementary materials for the paper that extends the conference paper : "Symboleo2SC: From Legal Contract Specifications to Smart Contracts" <code>symboleo-js-core</code> includes the implementation of the ontology of Symboleo. <code>Symboleo2SC-demo</code> includes the five evaluated Symboleo contracts, their generated smart contracts, and their unit tests.

Eason Chen, Niall Roche, Yuen‐Hsien Tseng, Walter Hernández · 6 authors

A Smart Legal Contract (SLC) is a specialized digital agreement comprising natural language and computable components. The Accord Project provides an open-source SLC framework containing three main modules: Cicero, Concerto, and Ergo. Currently, we need lawyers, programmers, and clients to work together with great effort to create a usable SLC using the Accord Project. This paper proposes a pipeline to automate the SLC creation process with several Natural Language Processing (NLP) models to convert law contracts to the Accord Project’s Concerto model. After evaluating the proposed pipeline, we discovered that our NER pipeline accurately detects CiceroMark from Accord Project template text with an accuracy of 0.8. Additionally, our Question Answering method can extract one-third of the Concerto variables from the template text. We also delve into some limitations and possible future research for the proposed pipeline. Finally, we describe a web interface enabling users to build SLCs. This interface leverages the proposed pipeline to convert text documents to Smart Legal Contracts by using NLP models.

Levy Marlon Souza Santiago

Submitted by Levy Santiago (levyssantiago@gmail.com) on 2023-09-22T11:33:39Z No. of bitstreams: 2 license_rdf: 1037 bytes, checksum: 996f8b5afe3136b76594f43bfda24c5e (MD5) Levy Santiago. Dissertacao Mestrado.pdf: 1690608 bytes, checksum: d786948994ce08676e7c4ca08284546b (MD5)

유영운

탈중앙화된 자율조직은 블록체인 네트워크에 기반하여 활동하는 국제적인 단체로서 이사회와 같은 경영진이 아닌 구성원들이 직접 자동화된 의사결정시스템을 이용하여 의사결정을 내리고, 그 같은 단체의사에 따라 운영되는 인적 단체이다. 이들이 탈중앙화된 가버넌스를 구축하는 과정은 사단이 정관 기타 규약을 마련하고 그에 따라 조직을 갖춰서 독립된 사회적 실체로 인정받는 과정과 유사한 면이 있다. 따라서 탈중앙화된 자율조직은 사단의 성격을 가지고 있다고 볼 수 있다. 국제적인 단체로서의 성격을 띄는 탈중앙화된 자율조직의 단체법적 지위를 판단하기 위해서는 국제조약과 국제사법상의 쟁점을 고려해야 한다. 다만, 외국의 경우, 대체로 이들을 조합이나 파트너쉽과 유사한 단체로 보아 구성원의 유한책임을 부정하는 경향이 있다. 그리하여 그 구성원들에게 유한책임을 인정하고 탈중앙화된 자율조직의 독립된 사회적 실체로서의 지위를 인정하기 위해, 이들을 유한책임회사로 인정하려는 입법례가 생기기도 하였다. 이들 조직을 우리나라 단체법 관점에서 살펴보는 경우, 법인 아닌 사단으로 평가할 수 있을지를 살펴볼 필요가 있다. 왜냐하면, 이들이 법인 아닌 사단으로 취급되는 경우, 총유규정에 따라 구성원의 책임재산이 분리되어 사실상 유한책임과 같은 효과를 얻을 수 있기 때문이다. 다만, 문제는 탈중앙화된 자율조직이 가지고 있는 특징들을 우리 민법상의 법인 아닌 사단의 법리로 포섭할 수 있는지 여부이다. 예를 들면, 대표자가 선임되어 있지 않고, 구성원지위 득실 변경을 가버넌스 토큰이라는 가상자산과 연동시키고, 조직이 관리하는 가상자산을 구성원들에게 분배하고, 영리 목적으로 운영되더라도 이를 민법상 법인 아닌 사단으로 취급할 수 있을지를 살펴보아야 한다. 계속해서 온라인을 기반으로 하여 다양한 국제적 성격을 띈 단체가 생겨날 것이다. 이들이 법인격을 취득하지 못한 경우, 그 속인법을 어떻게 결정하고, 국내 단체법상 이들을 어떻게 평가해야 할지가 고민될 수 밖에 없다. 우리 법인 아닌 사단 제도에 대한 비판이 있기는 하지만, 민법 개정 작업이 마무리되기 전까지는 이 같은 단체의 국내 단체법상 지위를 살펴볼 때 우리나라 특유의 법인 아닌 사단으로 취급할 수 있을지 그리고 그 실익이 무엇일지도 고민해 볼 필요가 있다. A Decentralized Autonomous Organization (DAO) is an international organization that operates based on a blockchain network. It is a member-managed association that its members make decisions using an automated decision-making system by themselves without the board of directors and is operated according to the groups decision. The process of establishing decentralized governance is similar to the process in which an association is recognized as an independent social entity by establishing its organs according to its articles and bylaws. Therefore, a Decentralized Autonomous Organization can be seen as having the characteristics of an association. The governing law of DAO shall be determined by international treaties and Conflict of Laws. Foreign countries seem to have a tendency to deny DAO members' limited liability by viewing DAO as an entity similar to partnership. In order to allow limited liability to its members, some jurisdictions made new law to recognize DAO as LLC. When examinging DAO from Korean entity law perspective, it is necessary to consider whether they can be evaluated as an unincorporated association. If DAOs can be treated as an unincorporated association, DAOs creditor can not be reimbursed from members asset, the same effect as limited liability. However, the question is whether characteristics of DAO are permissible under the legal principles of traditional unincorporated association. For example, it is also necessary to consider whether its activities would fall under the legal frame of unincorporated association laws in Korea as DAO has no representative, distribute virtual asset to its members, and even can be operated for profit. There will continue to emerge various online based international entities. If they are not incorporated in any jurisdiction, it is necessary evaluate their legal status from the Korean unincorporated entity law perspective based on Conflicts of Law approach, When evaluating their legal status, it is necessary to consider whether they can be treated as Koean unincorporated association and pros and cons as well.

Zhenzhou Tian, Xianqun Ke

No abstract is available for this record.

Marco Ortu, Giacomo Ibba, Claudio Conversano, Roberto Tonelli

No abstract is available for this record.

Bajeela Aejas, Abdelhak Belhi, Abdelaziz Bouras

No abstract is available for this record.

Uzay Işın Alıcı, Ayca Oksuztepe, Onur Kılınççeker, Enis Karaarslan

Decentralized applications (Dapps) have the potential to revolutionize many systems and are increasingly used, eg. in Web3 solutions. Smart contracts often manage valuable assets and sensitive data as the loss of any digital asset can be irreversible. There is a growing need for the security of these systems as any vulnerability can lead to irreversible financial losses. However, traditional software development and testing systems fall short of providing security for Blockchain technologies and Web3 developers. Considering the current potential of artificial intelligence, it can be used as a solution to secure Dapps. LLMs can analyze smart contract code for vulnerabilities, generate test cases, and provide recommendations for improvement. In this article, we question the use of ChatGPT for this purpose. It is shown that ChatGPT has the potential to aid developers. Advantages, limitations and improvement methods are given. Possible future work is given.

Nenad Petrović, Issam Al-Azzoni

No abstract is available for this record.

Youwei Huang, Sen Fang, Jianwen Li, Bin Hu · 6 authors

In recent years, research in software security has concentrated on identifying vulnerabilities in smart contracts to prevent significant losses of crypto assets on blockchains. Despite early successes in this area, detecting developers' intents in smart contracts has become a more pressing issue, as malicious intents have caused substantial financial losses. Unfortunately, existing research lacks effective methods for detecting development intents in smart contracts. To address this gap, we propose \textsc{SmartIntentNN} (Smart Contract Intent Neural Network), a deep learning model designed to automatically detect development intents in smart contracts. \textsc{SmartIntentNN} leverages a pre-trained sentence encoder to generate contextual representations of smart contracts, employs a K-means clustering model to identify and highlight prominent intent features, and utilizes a bidirectional LSTM-based deep neural network for multi-label classification. We trained and evaluated \textsc{SmartIntentNN} on a dataset containing over 40,000 real-world smart contracts, employing self-comparison baselines in our experimental setup. The results show that \textsc{SmartIntentNN} achieves an F1-score of 0.8633 in identifying intents across 10 distinct categories, outperforming all baselines and addressing the gap in smart contract detection by incorporating intent analysis.

Zhenzhou Tian, Yaqian Huang, Jie Tian, Zhongmin Wang · 6 authors

Smart contracts are programs that run on a blockchain, where Ethereum is one of the most popular ones supporting them. Due to the fact that they are immutable, it is essential to design smart contracts bug-free before they are deployed. However, various defects have been found in the deployed smart contracts, causing huge economic losses and lowing people's trust. Writing secure smart contracts is far from trivial, where developers tend to engage in reliable resources or social coding platforms to reuse code. This leads to a large number of similar contracts with potential security risks. Therefore, detecting similarity of smart contracts helps to avoid vulnerabilities, identify threats, and improve the security of Ethereum. In this paper, we design a learning-effective and costefficient model, called SmartSD, for Ethereum smart contract similarity detection. Different from the current research efforts, SmartSD is performed on a bytecode level and leverages deep neural networks to learn the latent representations from the opcode sequences for smart contract bytecodes, where the representation learning and similarity measurement are supervised via siamese neural networks. The experimental evaluations demonstrate that SmartSD outperforms EClone's 93.27% accuracy, achieving 98.37% high detection accuracy and 0.9850 F1-score, which is computationally tractable and effectively mitigates the interference caused by compilers.

Michele Soavi, Nicola Zeni, John Mylopoulos, Luisa Mich

Abstract The opportunity to automate and monitor the execution of legal contracts is gaining increasing interest in Business and Academia, thanks to the advent of smart contracts, blockchain technologies, and the Internet of Things. A critical issue in developing smart contract systems is the formalization of legal contracts, which are traditionally expressed in natural language with all the pitfalls that this entails. This paper presents a systematic literature review of papers for the main steps related to the transformation of a legal contract expressed in natural language into a formal specification. Key research studies have been identified, classified, and analyzed according to a four-step transformation process: (a) structural and semantic annotation to identify legal concepts in text, (b) identification of relationships among concepts, (c) contract domain modeling, and (d) generation of a formal specification. Each one of these steps poses serious research challenges that have been the subject of research for decades. The systematic review offers an overview of the most relevant research efforts undertaken to address each step and identifies promising approaches, best practices, and existing gaps in the literature.

Yu Tong, Weiming Tan, Jingzhi Guo, Bingqing Shen · 6 authors

In the last decade, blockchain smart contracts emerged as an automated, decentralized, traceable, and immutable medium of value exchange. Nevertheless, existing blockchain smart contracts are not compatible with legal contracts. The automatic execution of a legal contract written in natural language is an open research question that can extend the blockchain ecosystem and inspire next-era business paradigms. In this paper, we propose an AI-assisted Smart Contract Generation (AIASCG) framework that allows contracting parties in heterogeneous contexts and different languages to collaboratively negotiate and draft the contract clauses. AIASCG provides a universal representation of contracts through the machine natural language (MNL) as the common understanding of the contract obligations. We compare the design of AIASCG with existing smart contract generation approaches to present its novelty. The main contribution of AIASCG is to address the issue in our previous proposed smart contract generation framework. For sentences written in natural language, existing framework requires editors to manually split sentences into words with semantic meaning. We propose an AI-based automatic word segmentation technique called Separation Inference (SpIn) to fulfill automatic split of the sentence. SpIn serves as the core component in AIASCG that accurately recommends the intermediate MNL outputs from a natural language sentence, tremendously reducing the manual effort in contract generation. SpIn is evaluated from a robustness and human satisfaction point of view to demonstrate its effectiveness. In the robustness evaluation, SpIn achieves state-of-the-art F1 scores and Recall of Out-of-Vocabulary (R_OOV) words on multiple word segmentation tasks. In addition, in the human evaluation, participants believe that 88.67% of sentences can be saved 80–100% of the time through automatic word segmentation.

Madeleine Maslin, Joshua Butler

This chapter investigates how those in working in law and technology can and should work together to develop superior legal technologies, particularly in the context of legally binding smart contracts. It looks at contemporary models of interdisciplinary collaboration—including global consortia and strategic partnerships between law firms and technology companies—and considers how existing strategies to build legal technology could be applied to the challenge of developing distributed ledger solutions such as smart legal contracts. Working in tandem, lawyers and developers can build, deploy and scale smart legal contracts more efficiently; leveraging their expertise to break new ground, without diluting their core competencies. This approach will enhance the way lawyers work and democratize access to their services, while improving return on technology investment. By contrast, working in silos, these stakeholders risk misunderstanding part of the problem they are trying to solve. This chapter concludes by considering how lawyers can further stimulate the uptake of smart legal contracts by applying their skill-set to the development and adoption of international standards that are crucial for the sustainable growth and quality management of emerging legal technologies.

Michele Soavi, Nicola Zeni, John Mylopoulos, Luisa Mich

No abstract is available for this record.

Dor Bitan, Ran Canetti, Shafi Goldwasser, Rebecca Wexler

The use of hidden investigative software to collect evidence of crimes presents courts with a recurring dilemma: On the one hand, there is often clear public interest in keeping the software hidden to preserve its effectiveness in fighting crimes. On the other hand, criminal defendants have rights to inspect and challenge the full evidence against them, including law enforcement's investigative methods. In fact, in the U.S. adversarial legal system, the defendant's rights to scrutinize the government's tools are crucial to the truth-seeking process and to keeping law enforcement conduct lawful and constitutional. Presently, courts balance these conflicting interests on a case-by-case basis through evidentiary privilege law, often voicing their frustration with the challenging dilemma they face. We demonstrate how judicious use of a sophisticated cryptographic tool called Zero Knowledge Proofs (ZKPs) could help to mitigate this dilemma: Based on actual court cases where evidence was collected using a modified version of a peer-to-peer software, we demonstrate how law enforcement could, in these cases, augment their investigative software with a ZKP-based mechanism that would allow them to later provide full responses to challenges made by a defense expert -- and allow a defense expert to independently verify law enforcement claims -- while keeping the software hidden. We demonstrate the technical feasibility of our mechanism via a proof-of-concept implementation. We also propose legal analysis that justifies its use, discusses its merits, and considers the legal implications that the very existence of such a mechanism might have, even in cases where it has not been used. Our proof-of-concept may also extend to other verification dilemmas in the legal landscape.

Clark D. Asay

The software industry's history is also its future. Its history has been defined by both abundance and scarcity, and its future will be, too. In the 1970s and 80s, perceived software scarcity led U.S. legislators to formally grant intellectual property protections to software creators. Later, a different kind of scarcity-a lack of access to source code-led the founders of the free and open source software movement to flip intellectual property protections on their head in an effort to better promote abundance. That movement proved wildly successful, with today's software industry based on vast amounts of freely available open source software resources that both organizations and individuals collaboratively build. Abundance and scarcity will also define software's future, but in different ways. The abundance that the open source software movement spawned is in the midst of a significant commercial phase. That sometimes means that commercial competitors bring to the table a scarcity mindset that conflicts with the norms that made that movement so successful. Intellectual property concerns at times derail what may otherwise be even greater software abundance. And because so much software is moving into the Cloud, trade secrecy may become the software industry's most important form of intellectual property to the extent the industry abandons open models of innovation. The software industry's growing dependence on artificial intelligence (AI) is likely to contribute to these trends. The software industry is increasingly becoming synonymous with the AI industry, as more and more software companies either rely on AI in running their services or provide AI products to the public. As with all software, these AI technologies are increasingly provided from the Cloud, where trade secrecy is not only possible, but often preferable. But trade secrecy may be even more likely in the AI context because much of the magic in implementing AI systems lies in the know-how to piece them together from available open source software resources, decades-old AI techniques, and data. Hence, to the extent that software and AI technologists spurn open innovation in favor of a scarcity mindset, trade secrecy is likely to become its dominant form of legal protection. The advent of web3 technologies may eventually change some of these trends. But for now, increasing secrecy seems the most likely outcome. I conclude by arguing that this shift to secrecy is likely preferable to other forms of intellectual property.

Ilham Qasse, Shailesh Mishra, Mohammad Hamdaqa

The revolutionary potential of automatic code generation tools based on Model-Driven Engineering (MDE) frameworks has yet to be realized. Beyond their ability to help software professionals write more accurate, reusable code, they could make programming accessible for a whole new class of non-technical users. However, non-technical users have been slow to embrace these tools. This may be because their concrete syntax is often patterned after the operations of textual or graphical interfaces. The interfaces are common, but users would need more extensive, precise and detailed knowledge of them than they can be assumed to have, to use them as concrete syntax. Conversational interfaces (chatbots) offer a much more accessible way for non-technical users to generate code. In this paper, we discuss the basic challenge of integrating conversational agents within Model-Driven Engineering (MDE) frameworks, then turn to look at a specific application: the auto-generation of smart contract code in multiple languages by non-technical users, based on conversational syntax. We demonstrate how this can be done, and evaluate our approach by conducting user experience survey to assess the usability and functionality of the chatbot framework.

Xing Hu, Zhipeng Gao, Xin Xia, David Lo · 5 authors

Smart contracts have obtained much attention and are crucial for automatic financial and business transactions. For end-users who have never seen the source code, they can read the user notice shown in end-user client to understand what a transaction does of a smart contract function. However, due to time constraints or lack of motivation, user notice is often missing during the development of smart contracts. For end-users who lack the information of the user notices, there is no easy way for them to check the code semantics of the smart contracts. Thus, in this paper, we propose a new approach SMARTDOC to generate user notice for smart contract functions automatically. Our tool can help end-users better understand the smart contract and aware of the financial risks, improving the users’ confidence on the reliability of the smart contracts. SMARTDOC exploits the Transformer to learn the representation of source code and generates natural language descriptions from the learned representation. We also integrate the Pointer mechanism to copy words from the input source code instead of generating words during the prediction process. We extract 7,878 〈function, notice〉 pairs from 54,739 smart contracts written in Solidity. Due to the limited amount of collected smart contract functions (i.e., 7,878 functions), we exploit a transfer learning technique to utilize the learned knowledge to improve the performance of SMARTDOC. The learned knowledge obtained by the pre-training on a corpus of Java code, that has similar characteristics as Solidity code. The experimental results show that our approach can effectively generate user notice given the source code and significantly outperform the state-of-the-art approaches. To investigate human perspectives on our generated user notice, we also conduct a human evaluation and ask participants to score user notice generated by different approaches. Results show that SMARTDOC outperforms baselines from three aspects, naturalness, informativeness, and similarity.

Bajeela Aejas, Abdelaziz Bouras

Blockchain is in its way of revolutionizing different sectors with its decentralized peer-to-peer networking. Smart contracts are the piece of software that have written rules to be executed automatically to update the state of the block chain in a systematic way. One of the main use of Smart contract is in Supply Chain management. Supply Chain management deals with lot of legal contracts at a time. Contracts are agreements between two or more parties that define the duties and obligations for execution of any kind of activities. In this research, we are trying to automate the supply chain related contracts by identifying the important entities such as contract type, start date, end date etc., by using Natural Language Processing methods, then convert the contract to smart contract. This provides an efficient template for creation of smart contracts from natural language contracts and thereby offer best smart contract template for a given type of contract in Supply Chain.

Robert Herian

During this chapter I offer a brief account of technologies with legal education and present a case for the importance of studying technological effects on law. At the heart of that relationship is a tension between the latter (slowly) recognizing the unavoidability of the former in its future. The following discussion draws on aspects of my research on blockchains within Anglo-American common law jurisdictions, and my wider research on law, data, and technologies. Distributed ledger technologies (DLTs), of which blockchains are a species, are a form of ICT infrastructure used across commercial and civic sectors. The Bank of England explains the operation of DLTs as ‘a database architecture which enables the keeping and sharing of records in a distributed and decentralized way, while ensuring its integrity through the use of consensus-based validation protocols and cryptographic signatures.’ In reality, the bulk of present DLT use-cases involve financial services. These ‘ledgers’ are cryptographically secure databases for storing and recording novel forms of digital property (e.g. tokenized securities), relying on quasi-legal forms, chiefly ‘smart contracts’, to transact across networks without prejudice. Here I am interested in two aspects of DLTs. First, how DLTs and their stakeholders make use of and exert control over particular legal forms and vernacular (i.e. property, contracts, etc.). Second, how legal education, in providing the underpinnings for legal practice and helping to shape legal ideas, confronts and deals with technological phenomena such as DLTs to ensure the integrity of tomorrow’s lawyers and legal thinkers.