The integration of technologies like the Internet of Things (IoT), Big data, and Artificial Intelligence (AI) has empowered modern vehicles with the ability to communicate with one another for better cooperation on the roads. However, the communication between vehicles exposes the whole intelligent transportation system to new attack vectors. Malicious vehicles can spread misleading information, which, if acted upon, might result in traffic congestion, accidents, chaos, and even fatalities. As a countermeasure, the European Telecommunications Standards Institute (ETSI) proposes a framework, TR 103 460, for reporting abnormal behavior. However, there are many shortcomings, such as the absence of a peer-to-peer (P2P) misbehavior reporting (MR) service and the inability to secure the reporter's identity and reported information. To protect vehicles from abuse, we propose a P2P non-interactive zero-knowledge proof-based privacy-preserving MR framework. Upon testing, we found that the proposed framework prevented the disclosure of the reporter's identity and information and reduced the ITS-Stations' (ITS-Ss) exposure to misbehavior by 67.7% and 79.2% in suburban and highway traffic scenarios, respectively.
Ryan Zarick, Bryan Pellegrino, Isaac Zhang, Thomas Kim · 5 authors
We present ColorFloat, a family of O(1) space complexity algorithms that solve the problem of attributing (coloring) fungible tokens to the entity that minted them (minter). Tagging fungible tokens with metadata is not a new problem and was first formalized in the Colored Coins protocol. In certain contexts, practical solutions to this challenge have been implemented and deployed such as NFT. We define the fungible token coloring problem, one specific aspect of the Colored Coins problem, to be the problem of retaining fungible characteristics of the underlying token while accurately tracking the attribution of fungible tokens to their respective minters. Fungible token coloring has a wide range of Web3 applications. One application which we highlight in this paper is the onchain yield-sharing collateral-based stablecoin.
Bernhard Fisseni, Deniz Sarikaya, Bernhard Schröder
Abstract We discuss conceptual change and progress within mathematics, in particular how tools, structural concepts and representations are transferred between fields that appear to be unconnected or remote from each other. The theoretical background is provided by the frame concept, which is used in linguistics, cognitive science and artificial intelligence to model how explicitly given information is combined with expectations deriving from background knowledge. In mathematical proofs, we distinguish two kinds of frames, namely structural frames and ontological frames. The interaction between both kinds of frames can drive mathematical interpretation. We first discuss two examples where structural frames (formulaic notation) drive ontological development (the discovery or exploration of mathematical objects). The development of Boole’s Boolean algebra may at first appear as a metaphorical treatment of the (then) new area of logic. In the analysis, we discuss how different (aspects of) certain algebraic frames change in the transfer, how arising difficulties are solved and overall argue that Boole uses the numerical algebra frame as a research template for the discovery of a system for calculations in logic. Following Ifrah, we analyse the discovery of zero as an extension to the number ontology as driven by the development of notation. Both structural and ontological frames are extended and simplified as notation progresses. Finally, we discuss two examples from infinite combinatorics, viz. topological graph theory, and one foundational issue. In both examples, the two simultaneous frames about one object are maintained independently. They motivate different research questions, but may also fruitfully interact: shifting between multiple synchronously maintained perspectives acts as a motor of innovation. The analysis shows how a frame-based approach allows to model how different perspectives drive mathematical innovation because they highlight different aspects, questions and heuristics.
Julião Braga, Francisco Regateiro, Itana Stiubiener, Juliana Cristina Braga
This work presents a manually built ontology to aggregate and knowledge systematization of Decentralized Autonomous Organizations (DAOs) obtained from web pages. An ontology is a formal description of knowledge as a set of concepts within a domain and the relationships between them, providing a common vocabulary for researchers to share information. Ontology construction from text involves analyzing collected text, identifying relevant terms and concepts, and representing the ontology using representation languages such as OWL, RDF, or RDFS. Manual ontology construction can be performed using applications such as Protege. This work describes the methodology used, how to use the ontology created through Protege using SPARQL, and presents future work proposals, including creating the same ontology using Deep Learning from Machine Learning techniques.
The adoption of the General Data Protection Regulation (GDPR) has resulted in a significant shift in how the data of European Union citizens is handled. A variety of data sharing challenges in scenarios such as smart cities have arisen, especially when attempting to semantically represent GDPR legal bases, such as consent, contracts and the data types and specific sources related to them. Most of the existing ontologies that model GDPR focus mainly on consent. In order to represent other GDPR bases, such as contracts, multiple ontologies need to be simultaneously reused and combined, which can result in inconsistent and conflicting knowledge representation. To address this challenge, we present the smashHitCore ontology. smashHitCore provides a unified and coherent model for both consent and contracts, as well as the sensor data and data processing associated with them. The ontology was developed in response to real-world sensor data sharing use cases in the insurance and smart city domains. The ontology has been successfully utilised to enable GDPR-complaint data sharing in a connected car for insurance use cases and in a city feedback system as part of a smart city use case.
Ioannis Kourouklides, Novak I. Zukowski, Kleitos Alexandrou
This whitepaper presents the vision, mission, and overarching role of the GUT-AI Foundation. The Foundation aims to promote the research, development and eventually the deployment of user-friendly, human-centred and developer-friendly Artificial Intelligence (AI) systems for the betterment of humanity through an Ecosystem of Concepts and Implementations (ECI). The Foundation recognizes the potential of AI to revolutionize numerous industries, ranging from Healthcare and Education to Financial Services and Self-Driving Cars. However, the development of such AI systems poses significant challenges and impediments, such as multiple single points of failure, lack of interoperability, and lack of user adoption. Therefore, this whitepaper proposes a multidimensional approach to promote a whole ecosystem that has the ability to overcome such challenges. Primarily, the Foundation will encourage research into AI systems that are accessible, intuitive, and ready-to-use. The research will focus on proposing AI system architectures that meet the needs of the users, while they address their pain points in order to enhance both the User Experience (UX) and Developer Experience (DX). Furthermore, the Foundation will focus on promoting the adoption of best practices and Optional Open Standards for AI development and deployment that is automated, cost-effective, and scalable. For instance, these best practices will include the use of modular architectures, microservices, and containerization. By adopting these practices, the Foundation aims to enable interoperability and reuse of AI components. In addition, the Foundation envisions creating a marketplace, which will enable buyers to discover and adopt AI Solutions that meet their needs and preferences. The marketplace will also provide sellers with opportunities to showcase their AI Solutions, reach out to potential buyers and receive feedback from them. Finally, the Foundation will leverage emerging technologies such as Blockchain and Decentralized Autonomous Organizations (DAOs) to enhance the transparency, security, and trustworthiness of AI systems, while incentivizing innovation and collaboration among humans.
This PhD project proposes the theoretical and technological foundations of an approach for decentralized processing of streaming knowledge graphs, where autonomous reasoners may combine individual and collective processing of continuous data. These decentralized stream processors shall be capable of sharing not only data stream knowledge, but also processing duties, using collaboration and negotiation protocols. Moreover, commonly agreed semantic vocabularies will be used to address the high dynamicity of reasoners' knowledge and goals. The approach proposed in this project goes beyond previous works on stream reasoning, enabling the self-organization and coordination among distributed stream reasoners, based on techniques and principles inspired by Multi-Agent systems. On the one hand, it adds the ability to explicate processing goals, capabilities and knowledge, while on the other it exploits potential ways of interconnecting them in ways that expand their combined capacity/efficacy for managing highly dynamic flows of streaming knowledge. Through this approach, efficient local stream processors can establish cooperative processing schemes, respecting data privacy restrictions and data locality requirements through the exchange of streaming Knowledge Graphs.
Fabian Kirstein, Anton Altenbernd, Sonja Schimmler, Manfred Hauswirth
The Data Catalogue Vocabulary (DCAT) standard is a popular RDF vocabulary for publishing metadata about data catalogs and a valuable foundation for creating Knowledge Graphs. It has widespread application in the (Linked) Open Data and scientific communities. However, DCAT does not specify a robust mechanism to create and maintain persistent identifiers for the datasets. It relies on Internationalized Resource Identifiers (IRIs), that are not necessarily unique, resolvable and persistent. This impedes findability, citation abilities, and traceability of derived and aggregated data artifacts. As a remedy, we propose a decentralized identifier registry where persistent identifiers are managed by a set of collaborative distributed nodes. Every node gives full access to all identifiers, since an unambiguous state is shared across all nodes. This facilitates a common view on the identifiers without the need for a (virtually) centralized directory. To support this architecture, we propose a data model and network methodology based on a distributed ledger and the W3C recommendation for Decentralized Identifiers (DID). We implemented our approach as a working prototype on a five-peer test network based on Hyperledger Fabric.
Sotiris P. Gayialis, Evripidis P. Kechagias, Georgios Α. Papadopoulos
Undoubtedly, supply chain operations management is becoming more demanding for product tracing due to regulatory frameworks and fraudulent incidents. Therefore, rapidly increasing challenges arise in transparency, tracking, and data storage in many product supply chains. Especially in the case of wine supply chains, timely and accurate traceability is vital, as this industry is plagued by frequent counterfeiting activities that pose significant risks to both consumer health and business viability and prosperity. Blockchain technology can serve as a crucial aid for constructing modern wine supply chains by offering increased security and trust between all supply chain stakeholders. This paper presents a framework for developing Ethereum-based Blockchain distributed applications and demonstrates a use case for creating such an application for a wine traceability system. The developed distributed application enables the participants of the wine supply chain to track wines by adding and monitoring data about each individual wine bottle's production, fermentation, aging, bottling, and distribution, providing full supervision of its production and distribution. The demonstrated use case shows the different types of users and their interactions with the system to fully comprehend the advantages it can offer to all supply chain stakeholders as well as consumers and controlling authorities.
Ingineria ontologică, împreună cu tehnologiile Web semantice, permit modelarea și dezvoltarea semantică a fluxului operațional necesar pentru proiectarea TB. Cel mai utilizat sistem de modelare blockchain prin reprezentarea abstractă, descrierea și definirea structurii, a proceselor, a informațiilor și a resurselor, este modelarea intreprinderilor. Modelarea intreprinderii utilizează ontologiile de domeniu folosind limbaje de reprezentare a modelului. Bitcoin este principalul sistem de plată peer-to-peer şi monedă digitală care folosește tehnologia blockchain.
Abstract An emerging approach that addresses data heterogeneity challenges is the mediator-based architecture allowing transparent access to the data stored in many sources. Due to the growing diversity of data sources, the data integration process becomes a performance and administrative bottleneck. When dealing with decentralized heterogeneous data sources, the mediator-based technique is typically used to integrate the data. It describes a collection of applications that follow a number of data sources. The importance of analyzing and synthesizing the collected data has lately increased for academics researching autonomous and heterogeneous software systems. This study uses a mediator-based information integration model to improve pattern-based reasoning and overcome grammatical problems with integrating diverse information from web sources (IATs). The main goal of this study is to improve the mediator design for usage in the mediator-based information integration paradigm in order to address the problems caused by syntactic heterogeneity. Using our recommended methodology and enhancement strategy, the proposed technique would choose the pertinent domain from a variety of vendor-related data sources and antiquated file systems and deliver the necessary information set from heterogeneous data sources. Our suggested extended design functions well in the online bookstore as well, where there are several data sources and antiquated file systems. Future applications of this research include providing a thorough syntactic method that must be used to integrate data from various sources within the same organization into any Executive Support System (ESS).
Mario Scrocca, Marco Comerio, Alessio Carenini, Irene Celino
The blockchain technology provides integrity and reliability of the information, thus offering a suitable solution to guarantee trustability in a multi-stakeholder scenario that involves actors defining business agreements. The Ride2Rail project investigated the use of the blockchain to record as smart contracts the agreements between different stakeholders defined in a multimodal transportation domain. Modelling an ontology to represent the smart contracts enables the possibility of having a machine-readable and interoperable representation of the agreements. On one hand, the underlying blockchain ensures trust in the execution of the contracts, on the other hand, their ontological representation facilitates the retrieval of information within the ecosystem. The paper describes the development of the Ride2Rail Ontology for Agreements to showcase how the concept of an ontological smart contract, defined in the OASIS ontology, can be applied to a specific domain. The usage of the designed ontology is discussed by describing the modelling as ontological smart contracts of business agreements defined in a ride-sharing scenario.
Timotej Knez, Domen Gašperlin, Marko Bajec, Slavko Žitnik
Knowledge graphs are commonly represented by ontology-based databases. Tracking the provenance of ontological changes and ensuring ontology consistency is important. In this work, we propose a transaction manager for ontology-based database manipulation that combines blockchain and Semantic Web technologies. The latter is used for the efficient querying and modification of data, whereas the blockchain is used for the secure storage and tracking of changes. The blockchain enables a decentralized setup and data restoration. We evaluate our solution by measuring cost and time. Our solution introduces some overhead for updates whereas querying works at the same speed as the underlying ontology database.
Célio Márcio Soares Ferreira, Charles Tim Batista Garrocho, Carlos Frederico Marcelo da Cunha Cavalcanti, Jorge Sá Silva · 5 authors
Blockchain is already advancing in journeys beyond cryptocurrency applications, and Ethereum, already called the world's computer, is going on a path that intends to reinvent the internet or Web 3.0, currently the leading platform for deploying so-called distributed applications (DApp). The growth of these Dapps in different spheres of society, as Smart Cities and Industry 4.0 IoT applications demand new proposals and models that integrate Ethereum and its ecosystem data with existing datasets on the traditional Web. This work presents our efforts to apply ontologies representing an Ethereum network and ecosystem using the Semantic Web model to extract and link its data. We show EthExtras a new ontology that extends and simplifies the EthOn, and as proof of concept and sample of use, we design a middleware web that exposed as RDF graphs the Ethereum data in soft real-time.
Human species is currently standing on a junction, where it shall decide how it will further develop new technologies. Within the rapid technological evolution during last hundred years, human was designing technology to support him in many challenges - to lift heavier things, to reach further distances, to heal more people. With introduction of IT technologies, numerous visionary high-tech solutions entered homes. High capacity modern communication technologies allow human not only to share large amount of data, but also not to share any information - people share bytes, but according to social scientists they don't share valuable information as they did in past. While i.e. standard for road vehicle brakes had 8 pages in 1970s, current print has more than 80, but does not contain 10 times more information. Human has silently entered an era, where technology is often created for technology, not for human. In today's daily life, it is much more difficult to find valid and valuable information in data smog. Similar applies to road transportation - a socio-technological system, whose control is widely decentralized. We are searching for solutions to enhance transport safety and to increase infrastructure throughput in new technologies, while we forget that transport is not the source of the problem, but the consequence of human's need to move. Thus, the solution is not on a technological level, but on a system organization level. One of the highly promoted technological solution is autonomous driving. However, if we study the literature we cannot find anything explaining the automation as the only right way and as an achievable solution to road transport problems. No in-depth interdisciplinary study exist about the influence of autonomous road transportation on the society. The numbers showing positive expectations have more economical and marketing background, rather than technological or social. Every new product or prototype is being promoted with standard marketing approach. MIT's social study on management in technological companies points out incompetent decision making on high levels of management primarily due to complexity of modern technological systems, which cannot be understood neither by one CEO, nor by number of narrow oriented specialists. Still, the evolution of autonomous systems in the past decade is at least interesting. We are already attacking physical limits of sensors. New algorithms have to pursue the chase for low energy demands. Yet, the human brain needs just about 25 Watts at full load, while it is able to perform highly complex and accurate probabilistic estimations. When driving a road vehicle, average driver does a mistake emerging into an accident once in ca. 270 000 km. This leads to an average driver's reliability of 99,99996% on a driven distance. According to several accident researches, majority of accidents is caused by minority of drivers. The proposed article is aiming to discuss the achievable safety benefits of autonomous driving in comparison with human driver and with changes on system level, thus pointing out the main pillars of comprehensive autonomous road transport study.
Blockchains are gaining momentum due to the interest of industries and people in \emph{decentralized applications} (Dapps), particularly in those for trading assets through digital certificates secured on blockchain, called tokens. As a consequence, providing a clear unambiguous description of any activities carried out on blockchains has become crucial, and we feel the urgency to achieve that description at least for trading. This paper reports on how to leverage the \emph{Ontology for Agents, Systems, and Integration of Services} ("\ONT{}") as a general means for the semantic representation of smart contracts stored on blockchain as software agents. Special attention is paid to non-fungible tokens (NFTs), whose management through the ERC721 standard is presented as a case study.
Abstract We introduce the probabilistic two-agent justification logic $\textsf {IPJ}$, a logic in which we can reason about agents that perform interactive proofs. In order to study the growth rate of the probabilities in $\textsf {IPJ}$, we present a new method of parametrizing $\textsf {IPJ}$ over certain negligible functions. Further, our approach leads to a new notion of zero-knowledge proofs.
We focus on a type of linguistic formal reasoning where the goal is to reason\nover explicit knowledge in the form of natural language facts and rules (Clark\net al., 2020). A recent work, named PRover (Saha et al., 2020), performs such\nreasoning by answering a question and also generating a proof graph that\nexplains the answer. However, compositional reasoning is not always unique and\nthere may be multiple ways of reaching the correct answer. Thus, in our work,\nwe address a new and challenging problem of generating multiple proof graphs\nfor reasoning over natural language rule-bases. Each proof provides a different\nrationale for the answer, thereby improving the interpretability of such\nreasoning systems. In order to jointly learn from all proof graphs and exploit\nthe correlations between multiple proofs for a question, we pose this task as a\nset generation problem over structured output spaces where each proof is\nrepresented as a directed graph. We propose two variants of a proof-set\ngeneration model, multiPRover. Our first model, Multilabel-multiPRover,\ngenerates a set of proofs via multi-label classification and implicit\nconditioning between the proofs; while the second model, Iterative-multiPRover,\ngenerates proofs iteratively by explicitly conditioning on the previously\ngenerated proofs. Experiments on multiple synthetic, zero-shot, and\nhuman-paraphrased datasets reveal that both multiPRover models significantly\noutperform PRover on datasets containing multiple gold proofs.\nIterative-multiPRover obtains state-of-the-art proof F1 in zero-shot scenarios\nwhere all examples have single correct proofs. It also generalizes better to\nquestions requiring higher depths of reasoning where multiple proofs are more\nfrequent. Our code and models are publicly available at\nhttps://github.com/swarnaHub/multiPRover\n