Showing papers by "Michael Schumacher published in 2019"

Trust in Tourism via Blockchain Technology: Results from a Systematic Review

Abstract: Trust-free and trust-regulated systems based on blockchain technology (BCT) are currently experiencing the maximum hype and promise to revolutionise entire domains. Tourism products (intangible services) are highly dependent on trust and reputation management that is traditionally centralised and delegated to “expected” reliable third-parties (e.g., TripAdvisor). Although BCT has only recently started approaching the tourism industry and being employed in real-world applications, the scientific community has already been extensively exploring the promises of BCT. Therefore, there is an impending need for organising and understanding current knowledge and formalise societal, scientific, and technological challenges of applying BCT in the tourism industry. This paper moves the first step, presenting a systematic scientific literature review of studies involving BCT for tourism purposes. Providing a comprehensive overview, actors, assumptions, requirements, strengths, and limitations characterising the state of the art are analysed. Finally, advantages and future challenges of applying BCT in the tourism area are discussed.

Explainable Multi-Agent Systems Through Blockchain Technology.

Abstract: Advances in Artificial Intelligence (AI) are contributing to a broad set of domains. In particular, Multi-Agent Systems (MAS) are increasingly approaching critical areas such as medicine, autonomous vehicles, criminal justice, and financial markets. Such a trend is producing a growing AI-Human society entanglement. Thus, several concerns are raised around user acceptance of AI agents. Trust issues, mainly due to their lack of explainability, are the most relevant. In recent decades, the priority has been pursuing the optimal performance at the expenses of the interpretability. It led to remarkable achievements in fields such as computer vision, natural language processing, and decision-making systems. However, the crucial questions driven by the social reluctance to accept AI-based decisions may lead to entirely new dynamics and technologies fostering explainability, authenticity, and user-centricity. This paper proposes a joint approach employing both blockchain technology (BCT) and explainability in the decision-making process of MAS. By doing so, current opaque decision-making processes can be made more transparent and secure and thereby trustworthy from the human user standpoint. Moreover, several case studies involving Unmanned Aerial Vehicles (UAV) are discussed. Finally, the paper discusses roles, balance, and trade-offs between explainability and BCT in trust-dependent systems.

Social Network Chatbots for Smoking Cessation: Agent and Multi-Agent Frameworks

Abstract: Asynchronous messaging is leading human-machine interaction due to the boom of mobile devices and social networks. The recent release of dedicated APIs from messaging platforms boosted the development of computer programs able to conduct conversations, (i.e., chatbots), which have been adopted in several domain-specific contexts. This paper proposes SMAG: a chatbot framework supporting a smoking cessation program (JDF) deployed on a social network. In particular, it details the single-agent implementation, the campaign results, a multi-agent design for SMAG enabling the modelization of personalized behavior and user profiling, and highlighting of coupling chatbot technology with and multi-agent systems. CCS CONCEPTS • Applied computing → Health informatics; • Information systems → Personalization; Social networks; • Human-centered computing → Web-based interaction; Natural language interfaces.

The Good, the Bad, and the Ethical Implications of Bridging Blockchain and Multi-Agent Systems

Abstract: The agent based approach is a well established methodology to model distributed intelligent systems. Multi-Agent Systems (MAS) are increasingly employed in applications dealing with safety and information critical tasks (e.g., in eHealth, financial, and energy domains). Therefore, transparency and the trustworthiness of the agents and their behaviors must be enforced. For example, employing reputation based mechanisms can promote the development of trust. Nevertheless, besides recent early stage studies, the existing methods and systems are still unable to guarantee the desired accountability and transparency adequately. In line with the recent trends, we advocate that combining blockchain technology (BCT) and MAS can achieve the distribution of the trust, removing the need for trusted third parties (TTP), potential single points of failure. This paper elaborates on the notions of trust, BCT, MAS, and their integration. Furthermore, to attain a trusted environment, this manuscript details the design and implementation of a system reconciling MAS (based on the Java Agent DEvelopment Framework (JADE)) and BTC (based on Hyperledger Fabric). In particular, the agents’ interactions, computation, tracking the reputation, and possible policies for disagreement-management are implemented via smart contracts and stored on an immutable distributed ledger. The results obtained by the presented system and similar solutions are also discussed. Finally, ethical implications (i.e., opportunities and challenges) are elaborated before concluding the paper.

Intelligent Health Care Data Management Using Blockchain: Current Limitation and Future Research Agenda

Abstract: Health care is undergoing a big data revolution, with vast amounts of information supplied from numerous sources, leading to major paradigm shifts including precision medicine and AI-driven health care among others. Yet, there still exist significant barriers before such approaches could be adopted in practice, including data integration and interoperability, data sharing, security and privacy protection, scalability, and policy and regulatory issues. Blockchain provides a unique opportunity to tackle major challenges in health care and biomedical research, such as enabling data sharing and integration for patient-centered care, data provenance allowing verification authenticity of the data, and optimization of some of the health care processes among others. Nevertheless, technological constraints of current blockchain technologies necessitate further research before mass adoption of blockchain-based health care data management is possible. We analyze context-based requirements and capabilities of the available technology and propose a research agenda and new approaches towards achieving intelligent health care data management using blockchain.

Towards Profile and Domain Modelling in Agent-Based Applications for Behavior Change

Abstract: Health support programs play a vital role in public health and prevention strategies at local and national levels, for issues such as smoking cessation, physical rehabilitation, nutrition, or to regain mobility. A key success factor in these topics is related to the appropriate use of behavior change techniques, as well as tailored recommendations for users/patients, adapted to their goals and the continuous monitoring of their progress. Social networks interactions and the use of multi-agent technologies can further improve the effectiveness of these programs, especially through personalization and profiling of users and patients. In this paper we propose an agent-based model for supporting behavior change in eHealth programs. Moreover, we identify the main challenges in this area, especially regarding profile and domain modeling profiles for healthcare behavioral programs, where the definition of goals, expectations and argumentation play a key role in the success of a intervention.

Semi-automatic Semantic Enrichment of Personal Data Streams.

Abstract: Current information technologies allow people to acquire personal data related to their health, lifestyle, behavior, and activities, often using wearable and mobile devices. Personal data management technologies have emerged recently, in order to cope with the requirements of this type of data, ranging from personal clouds to self-storage solutions. Pryv.io is a comprehensive solution for managing this particularly sensible type of data streams, focusing both on data privacy and decentralization. In this paper, we describe SemPryv, a system aiming at providing a semantization mechanism for enriching personal data streams with standardized specialized vocabularies from third-party providers. It relies on third providers of semantic concepts, and includes rule-based mechanisms for facilitating the semantization process. A full implementation of SemPryv has been produced, pluggable to the existing Pryv.io platform, showing the feasibility of the approach. 1 Context & Motivation Copyright ©2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). The increasing amount of generated personal data allows for the development of personalized applications in different domains, usually related to health, lifestyle, or everyday activities. These often rely on different sources and acquisition modalities, including wearable devices, sensors, domotic technologies, or self-reporting methods. In this context, it is essential to provide data privacy guarantees, in order to avoid unintended access or disclosure. This difficulty to address this challenge is further increased due to the streaming nature of many of these datasets, which require infrastructure designed to manage high-volume and high-velocity information flows. Pryv.io is a privacy-centric middleware, used as a robust data management foundation to develop risk-controlled mHealth, eHealth, and InsurTech applications with confidence and in respect to IT and regulatory requirements. Pryv.io is built based on two key pillars: decentralization and privacy. Unlike traditional 2 J.-P. Calbimonte et al. centralized solutions, Pryv.io stores each data account separately and independently, making it possible to be even deployed on its own server [3]. Furthermore, data access can be delegated in a modular way, providing token-based authorization, e.g. for tertiary use by a clinician. Data is organized as a hierarchy of streams, each containing a series of events of different nature and type. Given the large heterogeneity of data sources in these areas, and the velocity of the data, it becomes essential to provide the means for automatically categorizing them according to standard ontologies and vocabularies, especially in the health domain. Given the diversity of potential personal data sources (e.g. from time series of a smartwatch to health record annotations), the accurate semantization of the data is a primary concern in order to provide an added value over the collected information. This paper describes the SemPryv subsystem for stream data enrichment3. The goal of SemPryv is to provide semantization capabilities for the Pryv.io middleware, such that it can automatically propose semantic concepts, associated to the heterogeneous data streams managed by the platform. The data semantization makes it possible to enhance the data model, currently conformed by typed events. Associating high-level ontology concepts to the stream events enables new types of search and discovery functionalities in the middleware, which were not possible up to now. Also, it provides the means to link the Pryv datasets with existing standards and models used widely for cataloguing data in the health sector. In particular, the use of standards, such as HL7 FHIR [1], make it possible to export and share the Pryv.io data with other systems and applications, as long as it is annotated with semantic vocabularies. The system described in this paper focuses on both the service-oriented architecture of SemPryv and its interaction with existing ontology providers as BioPortal [4], as well as a dedicated UI that allows experts to confirm or choose from the semantics suggestions offered by the module. The implementation of the system shows the feasibility of our fully decentralized solution for semantization of personal data streams, relying on the widely used HL7 FHIR standards for interoperability. 2 SemPryv Architecture The Pryv.io middleware is used to manage large and diverse streams of data coming from external platforms, wearable devices, and health record systems. These streams are organized through identifiers and tags that are later used for searching and querying. While it is technically possible to export and make the Pryv datasets available to external applications in different formats, standards such as HL7 FHIR [1] impose the necessity of adding explicit semantic annotations to the Pryv.io data streams, for instance using the SNOMED-CT [2] vocabulary. While this semantization process could be carried out manually, it is unrealistic and too time consuming to be realizable. The SemPryv module enables the addition of semantics to the datasets, in an automatic, or semiautomatic manner. Given the decentralized nature of Pryv.io, multiple instances can be used in order to store and manage isolated data streams. SemPryv is designed 3 Available at: https://sempryv.ehealth.hevs.ch Semi-automatic Semantic Enrichment of Personal Data Streams 3 Fig. 1. Decentralized deployment in Pryv and proxy access through SemPryv: every Pryv.io instance can be enhanced with SemPryv, with an authorization token. to act as a proxy for these instances, being able to forward requests to Pryv.io through its REST API (Figure 1). By passing an authentication token and the domain within the request, SemPryv is able to access any Pryv.io instance, and add the semantic annotation/suggestion features, as well as the FHIR support. Fig. 2. SemPryv architecture: Prxv.io interactions with the SemPryv back-end and UI, as well as the ontology providers. The architecture of SemPryv is depicted in Figure 2. SemPryv has two main components: a back-end that exposes the core services as a REST API, and a UI for end-users and experts. Besides the proxy capabilities mentioned before, the SemPryv back-end can connect to a series of providers for semantic vocabularies and ontologies. These may include existing APIs such as BioPortal [4], or other collections of relevant ontologies. SemPryv is able to query these providers in order to suggest relevant ontology terms for a given Pryv stream, or hierarchy of streams, which can then be validated, or confirmed by an expert through the SemPryv Web UI. The Pryv.io metadata can be then updated according to these suggestions and annotations. Additionally, the SemPryv back-end includes endpoints dedicated for the import/export of HL7 FHIR-compliant data streams, represented as bundle collections of observations. 4 J.-P. Calbimonte et al. 3 SemPryv Suggestions & Annotations Fig. 3. SemPryv: User interface, including access to streams hierarchies, events, and their metadata. Fig. 4. SemPryv UI: suggested annotations obtained from the BioPortal provider: SNOMED-CT terms. The architecture presented previously describes how the different components of the system interact with each other. Concerning the semantization process itself, the SemPryv module is flexible enough to adapt to different types of situations. For users and data integrators, the SemPryv UI (Figure 4) exposes the proposed semantics, queried from the 3rd party providers (e.g. BioPortal). Then, these suggestions can optionally be confirmed by an administrator before being consolidated into its corresponding Pryv instance. This semi-automatic semantization makes it possible to have full control over the type of semantics to be assigned. As an example, a body-weight stream in the Pryv.io middleware can be modeled as the weight of an individual according to SNOMED-CT, codified as: SNOMED-CT:27113001. Notice that multiple annotations can be attached to a given stream, and that these annotations can be inherited recursively by sub-streams and events inside of its hierarchy. Furthermore, other custom 3rd party ontology/vocabulary providers can be configured in order to feed the system. In addition, SemPryv includes the possibility of using predefined rules expressed in its knowledge graph. These rules can be modified by administrators, and essentially allow the definition of close terms from different ontologies. For instance in the following example, the knowledge graph matches Pryv temperature streams to a SNOMET-CT code identified as: snomed-ct:386725007. Similarly the same is done for mass. Then, the system also allows to match these rules to certain stream paths, defined using regular expressions. Semi-automatic Semantic Enrichment of Personal Data Streams 5

Towards semantic models for proceeding and behavior change in eHealth applications

Abstract: Behavior change is a complex process in which people receive support in order to improve aspects of their behavior, for instance regarding their health or lifestyle. Although there exist several theoretical approaches to model behavior change, including abstractions that can be applied to different use-cases, these are not easily translated into reusable components that can be integrated into implementable systems for persuasion. This work discusses the need for an ontology-based approach to modelling interactions in eHealth systems, with the goal of achieving behavior change. This contribution includes an analysis of current modelling needs in behavior change, specially regarding: stages of change, motivation & ability factors, plans & actions, argumentation, and do-

A Startup Assessment Approach Based on Multi-Agent and Blockchain Technologies

Abstract: The dynamic nature of startups is linked to both high risks in investments as well as potentially important financial benefits. A key aspect to manage interactions among investors, experts, and startups, is the establishment of trust guarantees. This paper presents the formalization and implementation of a system enforcing trust in the startup assessment domain. To do so, an existing architecture has been extended, incorporating a multi-agent community and related interactions via private blockchain technology. The developed system enables a trust-based community, immutably storing, tracking, and monitoring the agents’ interactions and reputations.