Abstract Adopting shared data resources requires scientists to place trust in the originators of the data. When shared data is later used in the development of artificial intelligence (AI) systems or machine learning (ML) models, the trust lineage extends to the users of the system, typically practitioners in fields such as healthcare and finance. Practitioners rely on AI developers to have used relevant, trustworthy data, but may have limited insight and recourse. This article introduces a software architecture and implementation of a system based on design patterns from the field of self‐sovereign identity. Scientists can issue signed credentials attesting to qualities of their data resources. Data contributions to ML models are recorded in a bill of materials (BOM), which is stored with the model as a verifiable credential. The BOM provides a traceable record of the supply chain for an AI system, which facilitates on‐going scrutiny of the qualities of the contributing components. The verified BOM, and its linkage to certified data qualities, is used in the AI scrutineer, a web‐based tool designed to offer practitioners insight into ML model constituents and highlight any problems with adopted datasets, should they be found to have biased data or be otherwise discredited.

The quantity and capabilities of smart devices and sensors deployed as part of the Internet of Things (IoT) and accessible via remote microservices is set to rise dramatically as the provision of interactive data streaming increases. This introduces opportunities to rapidly construct new applications by interconnecting these microservices in different workflow configurations. The challenge is to discover the required microservices, including those from trusted partners and the wider community, whilst being able to operate robustly under diverse networking conditions. This paper outlines a workflow approach that provides decentralized discovery and orchestration of verifiably trustable services in support of multi-party operations. The approach is based on adoption of patterns from self-sovereign identity research, notably Verifiable Credentials, to share information amongst peers based on attestations of service descriptions and prior service usage in a privacy preserving and secure manner. This provides a dynamic, trust-based framework for ratifying and evaluating the qualities of different services. Collating these new service descriptions and integrating with existing decentralized workflow research based on vector symbolic architecture (VSA) provides an enhanced semantic search space for efficient and trusted service discovery that is necessary to support a diverse range of emerging edge-computing environments. An architecture for a dynamic decentralized service discovery system, is designed, and described through application to a scenario which uses trusted peers’ reported experiences of an anomaly detection service to determine service selection.

Blockchains have been around for more than ten years, and since 2015, a plethora of systems have been launched to target more flexible use cases. More recently, several enterprise blockchain systems, such as Consensys Quorum and Hyperledger Fabric, have been launched to make blockchain simpler to apply in complex organizational configurations. In this paper, we identify a specific Department of Defense use case, extrapolate requirements, and perform a thorough assessment of the different layers of the blockchain stack to identify the existing state of the art and undertake a gap analysis of the technology for this context. We describe a platform that meets many of these challenges and show how we architected, designed, and implemented a solution for this use case for deployment at NAVAIR. This solution connects transactions from two separate blockchain systems, Consensys Quorum and Hyperledger Fabric, by using a graph-based approach that preserves privacy while enabling full transparency across the military and supplier networks.

Reproducibility can be considered as one of the basic requirements to ensure that a given research finding is accurate and acceptable. This paper presents a new layered approach that allows scientific researchers to provide a) data to fellow researchers to validate research and b) proofs of research quality to funding agencies, without revealing sensitive details associated with the same. We conclude that by integrating smart contracts, blockchain technology, and self-sovereign identity into an automated system, it is possible to assert the quality of scientific materials and validate the peer review process without the need of a central authority.

Abstract Increased adoption of artificial intelligence (AI) systems into scientific workflows will result in an increasing technical debt as the distance between the data scientists and engineers who develop AI system components and scientists, researchers and other users grows. This could quickly become problematic, particularly where guidance or regulations change and once‐acceptable best practice becomes outdated, or where data sources are later discredited as biased or inaccurate. This paper presents a novel method for deriving a quantifiable metric capable of ranking the overall transparency of the process pipelines used to generate AI systems, such that users, auditors and other stakeholders can gain confidence that they will be able to validate and trust the data sources and contributors in the AI systems that they rely on. The methodology for calculating the metric, and the type of criteria that could be used to make judgements on the visibility of contributions to systems are evaluated through models published at ModelHub and PyTorch Hub, popular archives for sharing science resources, and is found to be helpful in driving consideration of the contributions made to generating AI systems and approaches toward effective documentation and improving transparency in machine learning assets shared within scientific communities.

The proliferation of innovative mobile services such as augmented reality, networked gaming, and autonomous driving has spurred a growing need for low-latency access to computing resources that cannot be met solely by existing centralized cloud systems. Mobile Edge Computing (MEC) is expected to be an effective solution to meet the demand for low-latency services by enabling the execution of computing tasks at the network edge, in proximity to the end-users. While a number of recent studies have addressed the problem of determining the execution of service tasks and the routing of user requests to corresponding edge servers, the focus has primarily been on the efficient utilization of computing resources, neglecting the fact that non-trivial amounts of data need to be pre-stored to enable service execution, and that many emerging services exhibit asymmetric bandwidth requirements. To fill this gap, we study the joint optimization of service placement and request routing in dense MEC networks with multidimensional constraints. We show that this problem generalizes several well-known placement and routing problems and propose an algorithm that achieves close-to-optimal performance using a randomized rounding technique. Evaluation results demonstrate that our approach can effectively utilize available storage, computation, and communication resources to maximize the number of requests served by low-latency edge cloud servers.

In this paper we describe our experience adopting the Research Object Bundle (RO-Bundle) format with BagIt serialization (BagIt-RO) for the design and implementation of "tales" in the Whole Tale platform. A tale is an executable research object intended for the dissemination of computational scientific findings that captures information needed to facilitate understanding, transparency, and re-execution for review and computational reproducibility at the time of publication. We describe the Whole Tale platform and requirements that led to our adoption of BagIt-RO, specifics of our implementation, and discuss migrating to the emerging Research Object Crate (RO-Crate) standard.

The proliferation of innovative mobile services such as augmented reality, networked gaming, and autonomous driving has spurred a growing need for low-latency access to computing resources that cannot be met solely by existing centralized cloud systems. Mobile Edge Computing (MEC) is expected to be an effective solution to meet the demand for low-latency services by enabling the execution of computing tasks at the network-periphery, in proximity to end-users. While a number of recent studies have addressed the problem of determining the execution of service tasks and the routing of user requests to corresponding edge servers, the focus has primarily been on the efficient utilization of computing resources, neglecting the fact that non-trivial amounts of data need to be stored to enable service execution, and that many emerging services exhibit asymmetric bandwidth requirements. To fill this gap, we study the joint optimization of service placement and request routing in MEC-enabled multi-cell networks with multidimensional (storage-computation-communication) constraints. We show that this problem generalizes several problems in literature and propose an algorithm that achieves close-to-optimal performance using randomized rounding. Evaluation results demonstrate that our approach can effectively utilize the available resources to maximize the number of requests served by low-latency edge cloud servers.

Traditional service-based applications, in fixed networks, are typically constructed and managed centrally and assume stable service endpoints and adequate network connectivity. Constructing and maintaining such applications in dynamic heterogeneous wireless networked environments, where limited bandwidth and transient connectivity are commonplace, presents significant challenges and makes centralized application construction and management impossible. In this demonstration we present an architecture which is capable of providing an adaptable and resilient method for on-demand decentralized construction and management of complex time-critical applications in such environments. The approach uses a Vector Symbolic Architecture (VSA) to compactly represent an application as a single semantic vector that encodes the service interfaces, workflow, and the time-critical constraints required. By extending existing services interfaces, with a simple cognitive layer that can interpret and exchange the vectors, we show how the required services can be dynamically discovered and interconnected in a completely decentralized manner. There are a large number of workflow systems designed to work in various scientific domains, including support for the Internet of Things (IoT). One such workflow system is Node-RED, which is designed to bring workflow-based programming to IoT. The main focus of this demonstration is to show how we can migrate Node-RED workflows into a decentralized execution environment, so that such workflows can run on Edge networks.

The act of sharing scientific knowledge is rapidly evolving away from traditional articles and presentations to the delivery of executable objects that integrate the data and computational details (e.g., scripts and workflows) upon which the findings rely. This envisioned coupling of data and process is essential to advancing science but faces technical and institutional barriers. The Whole Tale project aims to address these barriers by connecting computational, data-intensive research efforts with the larger research process—transforming the knowledge discovery and dissemination process into one where data products are united with research articles to create “living publications” or tales. The Whole Tale focuses on the full spectrum of science, empowering users in the long tail of science, and power users with demands for access to big data and compute resources. We report here on the design, architecture, and implementation of the Whole Tale environment.

Privacy becomes one of the important issues in data-driven applications. The advent of non-PC devices such as Internet-of-Things (IoT) devices for data-driven applications leads to needs for light-weight data anonymization. In this paper, we develop an anonymization framework that expedites model learning in parallel and generates deployable models for devices with low computing capability. We evaluate our framework with various settings such as different data schema and characteristics. Our results exhibit that our framework learns anonymization models up to 16 times faster than a sequential anonymization approach and that it preserves enough information in anonymized data for data-driven applications.

Recently, a promising trend has evolved from previous centralized computation to decentralized edge computing in the proximity of end-users to provide cloud applications. To ensure the Quality of Service (QoS) of such applications and Quality of Experience (QoE) for the end-users, it is necessary to employ a comprehensive monitoring approach. Requirement analysis is a key software engineering task in the whole lifecycle of applications; however, the requirements for monitoring systems within edge computing scenarios are not yet fully established. The goal of the present survey study is therefore threefold: to identify the main challenges in the field of monitoring edge computing applications that are as yet not fully solved; to present a new taxonomy of monitoring requirements for adaptive applications orchestrated upon edge computing frameworks; and to discuss and compare the use of widely-used cloud monitoring technologies to assure the performance of these applications. Our analysis shows that none of existing widely-used cloud monitoring tools yet provides an integrated monitoring solution within edge computing frameworks. Moreover, some monitoring requirements have not been thoroughly met by any of them.

The Cloud computing paradigm is built on the concept of virtualization, allowing multiple virtual machines to cohabit on one physical device to enable the scaling up and down of applications through elastic on-demand provisioning. More recently containers e.g. Docker, have been shown to enable a more lightweight mechanism than hypervisors and proved to be a viable alternative for virtualization, based on shared operating systems. The advent of such lightweight environments has brought a multitude of application uses in research, science and industry, enabling pre-configured operating environments to be shared, reused and instantiated on demand. The sharing of containers has currently been exposed using centralized repositories (e.g. Dockerhub), which allows containers to be shared and to form the building blocks for further development. In this paper, we take a look at the next evolution of this lifecycle and consider whether it is viable to securely share container-based applications within a decentralized group of individuals and to provide an audit trail recording exactly who has shared what, and with whom. For this purpose we consider the use of Blockchain technologies, and consequently perform a comparative analysis of Blockchain technologies for this use case. The paper provides mostly a review and taxonomy of different ledger systems, which we believe may be of interest to the SafeData Workshop participants.

Time critical applications have very high requirements on network and computing services, in particular on well-tuned software architecture with sophisticated optimisation on data communication. Their development is often customised to dedicated infrastructure, and system performance is difficult to maintain when infrastructure changes. This fatal weakness in existing architecture and software tools causes very high development costs, and makes it difficult to fully utilise the virtualised, programmable and quality-on-demand services provided by networked Clouds to improve the system productivity. The Software Workbench for Interactive, Time Critical and Highly self-adaptive Cloud applications (SWITCH) is a newly funded project by EU H2020 to address this urgent industrial need, it aims at improving the existing development and execution model of time critical applications by introducing a novel conceptual model called application-infrastructure co-programming and control model, in which application QoS/QoE together with the programmability and controllability of Cloud environments can be all included in the complete lifecycle of applications.

In this paper we discuss a flexible distributed workflow-based approach that enables researchers to study biomedical data for creating decision support pipelines. Specifically we describe the TRIACS platform, which has first been applied to supporting evidence-based decisions for optimum diabetic retinopathy screening intervals In the prioritization mechanism, pseudonymised case data is stratified for screening need by computation of outcome risk or by clustering of dasiaat-riskpsila cases with past cases of actual preventable outcomes. Workflows present a novel approach to this problem by providing an appropriate level of granularity for breaking the domain problem into a collection of reusable service-oriented components that can be applied in different ways. TRIACS is intended to make the creation of new application logic quicker and easier than bespoke development methods. Through the TRIACS workflow interface, modular code is portable and available to solve analogous domain problems including application to trial studies for mining and analysing clinical data.

This paper discusses the Triana workflow system within the context of the workflow community at large. It provides a brief background for Triana and discusses the ways in which it is has been used in the past for serial as well as distributed tasks. A description of the Triana distributed architecture is given followed by a discussion of its key features, including its user interface and its ability to work simultaneously in heterogeneous distributed environments. The high-level Grid and service-based interfaces that enable this support are outlined along with their corresponding bindings to the underlying middleware, such as WSPeer, Jxta, P2PS, Globus and Web and WS-RF services. New directions are outlined within the peer-to-peer context, followed by a description of two current P2P application domains and two collaborations that provide distributed P2P simulations for testing P2P overlays for use in massively distributed processing and searching, before deployment.

Abstract In this paper, we discuss a real‐world application scenario that uses three distinct types of workflow within the Triana problem‐solving environment: serial scientific workflow for the data processing of gravitational wave signals; job submission workflows that execute Triana services on a testbed; and monitoring workflows that examine and modify the behaviour of the executing application. We briefly describe the Triana distribution mechanisms and the underlying architectures that we can support. Our middleware independent abstraction layer, called the Grid Application Prototype (GAP), enables us to advertise, discover and communicate with Web and peer‐to‐peer (P2P) services. We show how gravitational wave search algorithms have been implemented to distribute both the search computation and data across the European GridLab testbed, using a combination of Web services, Globus interaction and P2P infrastructures. Copyright © 2005 John Wiley & Sons, Ltd.

In this paper, we describe Triana, a distributed problem-solving environment that makes use of the Grid to enable a user to compose applications from a set of components, select resources on which the composed application can be distributed and then execute the application on those resources. We describe Triana's current pluggable architecture that can support many different modes of operation by the use of flexible writers for many popular Web service choreography languages. We further show, that the Triana architecture is middleware-independent through the use of the Grid Application Toolkit (GAT) API and demonstrate this through the use of a GAT binding to JXTA. We describe how other bindings being developed to Grid infrastructures, such as OGSA, can seamlessly be integrated within the current prototype by using the switching capability of the GAT. Finally, we outline an experiment we conducted using this prototype and discuss its current status. Copyright © 2005 John Wiley & Sons, Ltd.

Service composition refers to the aggregation of services to build complex applications to achieve client requirements. It is an important challenge to make it possible for users to construct complex workflows transparently and thereby insulating them from the complexity of interacting with numerous heterogeneous services. We present an extension to the Triana PSE to facilitate graphical Web service discovery, composition and invocation. Our framework has several novel features which distinguish it from other work in this area. First, users can graphically create complex service compositions. Second, Triana allows the user to share the composite service as a BPELAWS graph or expose it as a service in a one-click manner. Third, Triana allows the user to easily carry out "what-if" analysis by altering existing workflows. Fourth, Triana allows the user to record provenance data for a workflow. Finally, our framework allows the user to execute the composed graph on a Grid or P2P network. Triana is a part of the GridLab and GridOneD projects and is used in the GEO 600 project.

A "Consumer Grid" provides the individual-based counterpart to the organisation-based computational grid. We describe a peer-to-peer system for utilising computational resources on the Grid - extending existing work undertaken in systems such as Entropia and SETI@home. The potential of such a distributed computing resource has been in some ways demonstrated recently by the SETI@home project, having used over 650,000 years of CPU time at the time of writing. A user develops applications within such an environment using a visual workflow system called "Triana" - which automatically generates suitable code for distribution, and can support the user in making placement decisions for their modules. Triana will also be deployed as the workflow enactment engine along with the grid application toolkit (GAT) within the European GridLab project.

Most modern pitch-perception theories incorporate a pattern-recognition scheme to extract pitch. Typically, this involves matching the signal to be classified against a harmonic-series template for each pitch to find the one with the best fit. Although often successful, such approaches tend to lack generality and may well fail when faced with signals with much depleted or inharmonic components. Here, an alternative method is described, which uses an adaptive resonance theory (ART) artificial neural network (ANN). By training this with a large number of spectrally diverse input signals, we can construct more robust pitch-templates which can be continually updated without having to re-code knowledge already acquired by the ANN. The input signal is Fourier-transformed to produce an amplitude spectrum. A mapping scheme then transforms this to a distribution of amplitude within ‘semitone bins’. This pattern is then presented to an ARTMAP ANN consisting of an ART2 and ART1 unsupervised ANN linked by a map field. The system was trained with pitches ranging over three octaves (C3 to Cf) on a variety of instruments and developed a desirable insensitivity to phase, timbre and loudness when classifying.