Ontology matching aims at discovering mappings between the entities of two ontologies. It plays an important role in the integration of heterogeneous data sources that are described by ontologies. Interactive ontology matching involves domain experts in the matching process. In some approaches, the expert provides feedback about mappings between ontology entities, that is, these approaches select mappings to present to the expert who replies which of them should be accepted or rejected, so taking advantage of the knowledge of domain experts towards finding an alignment. In this paper, we present Alin, an interactive ontology matching approach which uses expert feedback not only to approve or reject selected mappings but also to dynamically improve the set of selected mappings, that is, to interactively include and to exclude mappings from it. This additional use for expert answers aims at increasing in the benefit brought by each expert answer. For this purpose, Alin uses four techniques. Two techniques were used in the previous versions of Alin to dynamically select concept and attribute mappings. Two new techniques are introduced in this paper: one to dynamically select relationship mappings and another one to dynamically reject inconsistent selected mappings using anti-patterns. We compared Alin with state-of-the-art tools, showing that it generates alignment of comparable quality.

Nakamoto proposed a new solution to transact value via the internet. And since 2009, blockchain technology has expanded and diversified. It has, however, proven to be inefficient in the way it achieves its outcomes, especially through the proof of work protocol. Other developers are promoting alternative methods but, as yet, none has superseded proof of work. The competing protocols illuminate a key feature of the blockchain community, namely, its inability to create consensus in a decentralized community. Because of this lack of consensus, the formation of standards is particularly difficult to achieve. At best standards are contested sites, and we examine three such sites where some form of agreement over standards will be essential if blockchain is to evolve successfully. These three sites are blockchain governance, smart contracts, and interoperability of blockchains. We argue that because standards’ formation is a contested and contingent process, the blockchain community will persist in creating difficulties and barriers for itself until it is able to resolve internal conflicts.

The popularity of smart cars is increasing around the world as they offer a wide range of services and conveniences. These smart cars are equipped with a variety of sensors generating a large amount of data, many of which are critical. Besides, there are multiple parties involved in the lifespan of a smart car, such as manufacturers, car owners, government agencies, and third-party service providers who also generate data about the vehicle. In addition to managing and sharing data among these entities in a secure and privacy-friendly way which is a great challenge itself, there exists a trust deficit about some types of data as they remain under the custody of the car owner (e.g. satellite navigation and mileage data) and can easily be manipulated. In this article, we propose a blockchain-assisted architecture enabling the owner of a smart car to create an immutable record of every data, called the autobiography of a car, generated within its lifespan. We also explain how the trust about this record is guaranteed by the immutability characteristic of the blockchain. Furthermore, the article describes how the proposed architecture enables a secure and privacy-preserving mechanism for sharing of smart car data among different parties.

The aim of this critical review paper is threefold: (a) to provide an insight on the impact of ontology engineering methodologies (OEMs) to the evolution of living and reused ontologies, (b) to update the ontology engineering (OE) community on the status and trends in OEMs and of their use in practice and (c) to propose a set of recommendations for working ontologists to consider during the life cycle of living, evolved and reused ontologies. The work outlined in this critical review paper has been motivated by the need to address critical issues on keeping ontologies alive and evolving while these are shared in wide communities. It is argued that the engineering of ontologies must follow a well-defined methodology, addressing practical aspects that would allow (sometimes wide) communities of experts and ontologists to reach consensus on developments and keep the evolution of ontologies ‘in track’. In doing so, specific collaborative and iterative tool-supported tasks and phases within a complete and evaluated ontology life cycle are necessary. This way the engineered ontologies can be considered ‘shared, commonly agreed and continuously evolved “live” conceptualizations’ of domains of discourse. Today, in the era of Linked Data and Knowledge Graphs, it is more necessary than ever not to neglect to consider the recommendations that OEMs explicitly and implicitly introduce and their implications to the evolution of living ontologies. This paper reports on the status of OEMs, identifies trends and provides recommendations based on the findings of an analysis that concerns the impact of OEMs to the status of well-known, widely used and representative ontologies.

This paper presents an approach for the enactment of policies in digital health based on our earlier work on the implementation of digital contracts in distributed systems. A formal policy model and an abstract policy language for the expression of healthcare policies are first proposed, leveraging the semantics of the ISO Reference Model for Open Distributed Processing enterprise language standard. Healthcare consent policies included in the HL7 Fast Health Interoperability Resource (FHIR®) standard are used to illustrate the modelling approach. Several distributed ledger and smart legal contract options were considered next as target platforms for implementation. Their benefits are highlighted along with considerations on their use reflecting business concerns of risk, trust and cost.

There is an implicit assumption in machine learning techniques that each new task has no relation to the tasks previously learned. Therefore, tasks are often addressed independently. However, in some domains, particularly reinforcement learning (RL), this assumption is often incorrect because tasks in the same or similar domain tend to be related. In other words, even though tasks are quite different in their specifics, they may have general similarities, such as shared skills, making them related. In this paper, a novel domain adaptation-based method using adversarial networks is proposed to do transfer learning in RL problems. Our proposed method incorporates skills previously learned from source task to speed up learning on a new target task by providing generalization not only within a task but also across different, but related tasks. The experimental results indicate the effectiveness of our method in dealing with RL problems.

The problem of traffic congestion incurs numerous social and economical repercussions and has thus become a central issue in every major city in the world. For this work we look at the transportation domain from a multiagent system perspective, where every driver can be seen as an autonomous decision-making agent. We explore how learning approaches can help achieve an efficient outcome, even when agents interact in a competitive environment for sharing common resources. To this end, we consider the route choice problem, where self-interested drivers need to independently learn which routes minimise their expected travel costs. Such a selfish behaviour results in the so-called user equilibrium, which is inefficient from the system’s perspective. In order to mitigate the impact of selfishness, we present Toll-based Q-learning (TQ-learning, for short). TQ-learning employs the idea of marginal-cost tolling (MCT), where each driver is charged according to the cost it imposes on others. The use of MCT leads agents to behave in a socially desirable way such that the is attainable. In contrast to previous works, however, our tolling scheme is distributed (i.e., each agent can compute its own toll), is charged a posteriori (i.e., at the end of each trip), and is fairer (i.e., agents pay exactly their marginal costs). Additionally, we provide a general formulation of the toll values for univariate, homogeneous polynomial cost functions. We present a theoretical analysis of TQ-learning, proving that it converges to a system-efficient equilibrium (i.e., an equilibrium aligned to the system optimum) in the limit. Furthermore, we perform an extensive empirical evaluation on realistic road networks to support our theoretical findings, showing that TQ-learning indeed converges to the optimum, which translates into a reduction of the congestion levels by 9.1%, on average.

Biodiversity research studies the variability and diversity of organisms, including variability within and between species with particular focus on the functional diversity of traits and their relationship to environment. Managing biodiversity data implies dealing with its heterogeneous nature using semantics and tailored ontologies. These are themselves differently conceived, and combining them in semantically enabled applications necessitates an effective alignment between their concepts. This paper describes the ontology matching of biodiversity- and ecology-related ontologies. We illustrate diverse challenges introduced by this kind of ontologies to ontology matching in general. Real use cases requiring pairwise alignments between environment and trait ontologies are introduced. We describe our experience creating a new track at the Ontology Alignment Evaluation Initiative designed for this specific domain and report on the results obtained by state-of-the-art participating systems. The biodiversity and ecology use case turns out to be a strong one for ontology matching, introducing new interesting challenges. Even if most of the matching systems perform relatively well in the proposed matching tasks, there is still room for improvement. We highlight possible directions in that matter and elaborate on our plan to further progress with the track.

This research demonstrates financial derivative trade of unprocessed materials, for the mining industry through legal smart contracts. Within the mining supply chain, a stock of mined resources can reside in a mineral stockpile for over twenty years without gaining financial interest and without undergoing the mineral extraction process to derive value from the asset. This research elaborates on a blockchain solution implemented to increase miners’ short-term cash flow for business operations through the issuance of derivative assets on mineral stockpiles which can be traded through legally binding smart contracts. The system is the first to enable mining companies’ access to the underlying asset’s value earlier in the production lifecycle through smart contract technology whilst providing hedge funds with access to new financial products for investment portfolios.

Recent progress in reinforcement learning (RL) using self-play has shown remarkable performance with several board games (e.g., Chess and Go) and video games (e.g., Atari games and Dota2). It is plausible to hypothesize that RL, starting from zero knowledge, might be able to gradually approach a winning strategy after a certain amount of training. In this paper, we explore neural Monte Carlo Tree Search (neural MCTS), an RL algorithm that has been applied successfully by DeepMind to play Go and Chess at a superhuman level. We try to leverage the computational power of neural MCTS to solve a class of combinatorial optimization problems. Following the idea of Hintikka’s Game-Theoretical Semantics, we propose the Zermelo Gamification to transform specific combinatorial optimization problems into Zermelo games whose winning strategies correspond to the solutions of the original optimization problems. A specially designed neural MCTS algorithm is then introduced to train Zermelo game agents. We use a prototype problem for which the ground-truth policy is efficiently computable to demonstrate that neural MCTS is promising.

Loyalty cards programs have been used by retailers to increase customer retention. Loyality cards provide means to identify a particular customer and to collect customer-specific data, thus enabling individualized marketing; however, operating a loyalty program is complicated for retailers since they require to manage balances, collections, and transfers of customers. This is exactly the same problem the retailers were facing before credit cards were readily available. A new problem is that customers now have too many cards, customers may forget, or even deliberately decide to carry only a selection of their cards. This paper proposes a loyalty program based on a blockchain that does not require a physical card for identifying customers as it associates customers to their phone numbers, since nowadays people always carry their phone. In this perspective, companies can reduce overhead costs associated to managing the loyalty program. This paper reviews the technology required and describes the implementation of a loyalty program based on blockchains. Finally, it also enumerates the reasons for choosing the blockchain technology for this application.

Ontology alignment is an important and inescapable problem for the interconnections of two ontologies stating the same concepts. Ontology alignment evaluation initiative (OAEI) has been taken place for more than a decade to monitor and help the progress of the field and to compare systematically existing alignment systems. As of 2018, the evaluation of systems is partly transitioned to the HOBBIT platform. This paper contains the description of our alignment system, simulated annealing-based ontology matching (SANOM), and its adaption into the HOBBIT platform. The outcomes of SANOM on the HOBBIT for several OAEI tracks are reported, and the results are compared with other competing systems in the corresponding tracks.

Distributed ledger technology (DLT) is regarded as a revolutionary solution that offers immutability, transparency, trust, and efficiency while ‘transcending law and regulation’. One of the potential applications of DLT is in the securities market. Share registration, settlement, regulatory compliance, information disclosure, payment systems, and market service requirements can be redesigned with the use of DLT. This paper will examine the impact that such changes will have on legal theories and governance, while also discussing the effects on enforcement techniques. In addition, general blockchain-legal issues will be critically analyzed in the context of securities markets.

The use of robots in performance arts is increasing. But, it is hard for robots to cope with unexpected circumstances during a performance, and it is almost impossible for robots to act fully autonomously in such situations. IROS-HAC is a new challenge in robotics research and a new opportunity for cross-disciplinary collaborative research. In this paper, we describe a practical method for generating different personalities of a robot entertainer. The personalities are created by selecting speech or gestures from a set of options. The selection uses roulette wheel selection to select answers that are more closely aligned with the desired personality. In particular, we focus on a robot magician, as a good magic show includes good interaction with the audience and it may also include other robots and performers. The magician with a variety of personalities increased the audience immersion and appreciation and maintained the audience’s interest. The magic show was awarded first prize in the competition for a comprehensive evaluation of technology, story, and performance. This paper contains both the research methodology and a critical evaluation of our research.

Network providers either attempt to handle massive distributed denial-of-service attacks themselves or redirect traffic to third-party scrubbing centers. If providers adopt the first option, it is sensible to counter such attacks in their infancy via provider collaborations deploying distributed security mechanisms across multiple domains in an attack path. This motivated our work presented in this paper. Specifically, we investigate the establishment of trusted federations among adjacent and disjoint network domains, that is, autonomous systems (ASes) that collectively mitigate malicious traffic. Our approach is based on Distributed Ledger Technologies for signaling, coordination, and orchestration of a collaborative mitigation schema via appropriate blockchain-based smart contracts. Reputation scores are used to rank ASes based on their mitigation track record. The allocation of defense resources across multiple collaborators is modeled as a combinatorial optimization problem considering reputation scores and network flow weights. Malicious flows are mitigated using programmable network data paths within the eXpress Data Path (XDP) framework; this enables operators with enhanced packet processing throughput and advanced filtering flexibility. Our schema was implemented in a proof-of-concept prototype and tested under realistic network conditions.

Blockchain technology has rapidly emerged as a decentralized trusted network to replace the traditional centralized intermediator. Especially, the smart contracts that are based on blockchain allow users to define the agreed behaviour among them, the execution of which will be enforced by the smart contracts. Based on this, we propose a decentralized booking system that uses the blockchain as the intermediator between hoteliers and travellers. The system enjoys the trustworthiness of blockchain, improves efficiency and reduces the cost of the traditional booking agencies. The design of the system has been formally modelled using the CSP# language and verified using the model checker Process Analysis Toolkit. We have implemented a prototype decentralized booking system based on the Ethereum ecosystem.

The Internet of Things (IoT) has recently emerged as an innovative technology capable of empowering various areas such as healthcare, agriculture, smart cities, smart homes and supply chain with real-time and state-of-the-art sensing capabilities. Due to the underlying potential of this technology, it already saw exponential growth in a wide variety of use-cases in multiple application domains. As researchers around the globe continue to investigate its aptitudes, a collective agreement is that to get the best out of this technology and to harness its full potential, IoT needs to sit upon a flexible network architecture with strong support for security, privacy and trust. On the other hand, blockchain (BC) technology has recently come into prominence as a breakthrough technology with the potential to deliver some valuable properties such as resiliency, support for integrity, anonymity, decentralization and autonomous control. Several BC platforms are proposed that may be suitable for different use-cases, including IoT applications. In such, the possibility to integrate the IoT and BC technology is seen as a potential solution to address some crucial issues. However, to achieve this, there must be a clear understanding of the requirements of different IoT applications and the suitability of a BC platform for a particular application satisfying its underlying requirements. This paper aims to achieve this goal by describing an evaluation framework which can be utilized to select a suitable BC platform for a given IoT application.

The software and hardware applications are clearly on the way of becoming an integral tool of business, communication and popular culture in many parts of the world. People are interacting with the environment via the Internet to perform physical activities remotely. These applications are hosted in the public or private servers under the control of the server admin. The users’ online usage data can be stored in public or private cloud platforms, used for processing and monitoring users’ online behaviour and emotional factors and shared with third parties to facilitate making their business decisions. When users allow their data to be collected via software applications and mobile devices, users need to have some level of trust and control over their data. But, software applications or mobile devices connected to the cloud server using client–server architecture does not ensure the reliability, security and integrity among their data. To get over these kinds of limitations, we propose a database management system using blockchain technology that can be used by any software applications. The blockchain database connected to the cloud server can be used to increase the trustfulness of the application. Blockchain has the capability to provide decentralization, immutability and owner-controlled digital assets among software applications. Since users can save their data in a shared transaction repository with tamper-resistant records, it enables related parties to access and control users’ data without the need for a central control system.