7,897 research outputs found

    Man and Machine: Questions of Risk, Trust and Accountability in Today's AI Technology

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    Artificial Intelligence began as a field probing some of the most fundamental questions of science - the nature of intelligence and the design of intelligent artifacts. But it has grown into a discipline that is deeply entwined with commerce and society. Today's AI technology, such as expert systems and intelligent assistants, pose some difficult questions of risk, trust and accountability. In this paper, we present these concerns, examining them in the context of historical developments that have shaped the nature and direction of AI research. We also suggest the exploration and further development of two paradigms, human intelligence-machine cooperation, and a sociological view of intelligence, which might help address some of these concerns.Comment: Preprin

    Envisioning and evolving: Future evolution of the concept and the practice of service design

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    ervice Design is evolving from an emerging field, breaking new ground in the design and service research areas, to a more mature stage, developing a set of fundamental concepts, methods and principles that can provide the foundation for its further significance and impact in both research and practice. This paper reflects on the roots and recent evolution of service design in terms of fundamental concepts, methods and outcomes, taking into account the papers in the Envisioning and Evolving track. It considers how the growing interrelation with close fields of service research is introducing useful “contaminations” and reports how the Service perspective is revealing its potential to bring life to technical and entrenched systems. It goes on to argue that design should aim to bring services to life to prove its real, distinguishing value and contribution

    Seafloor characterization using airborne hyperspectral co-registration procedures independent from attitude and positioning sensors

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    The advance of remote-sensing technology and data-storage capabilities has progressed in the last decade to commercial multi-sensor data collection. There is a constant need to characterize, quantify and monitor the coastal areas for habitat research and coastal management. In this paper, we present work on seafloor characterization that uses hyperspectral imagery (HSI). The HSI data allows the operator to extend seafloor characterization from multibeam backscatter towards land and thus creates a seamless ocean-to-land characterization of the littoral zone

    RADON: Rational decomposition and orchestration for serverless computing

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    Emerging serverless computing technologies, such as function as a service (FaaS), enable developers to virtualize the internal logic of an application, simplifying the management of cloud-native services and allowing cost savings through billing and scaling at the level of individual functions. Serverless computing is therefore rapidly shifting the attention of software vendors to the challenge of developing cloud applications deployable on FaaS platforms. In this vision paper, we present the research agenda of the RADON project (http://radon-h2020.eu), which aims to develop a model-driven DevOps framework for creating and managing applications based on serverless computing. RADON applications will consist of fine-grained and independent microservices that can efficiently and optimally exploit FaaS and container technologies. Our methodology strives to tackle complexity in designing such applications, including the solution of optimal decomposition, the reuse of serverless functions as well as the abstraction and actuation of event processing chains, while avoiding cloud vendor lock-in through models

    ImageJ2: ImageJ for the next generation of scientific image data

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    ImageJ is an image analysis program extensively used in the biological sciences and beyond. Due to its ease of use, recordable macro language, and extensible plug-in architecture, ImageJ enjoys contributions from non-programmers, amateur programmers, and professional developers alike. Enabling such a diversity of contributors has resulted in a large community that spans the biological and physical sciences. However, a rapidly growing user base, diverging plugin suites, and technical limitations have revealed a clear need for a concerted software engineering effort to support emerging imaging paradigms, to ensure the software's ability to handle the requirements of modern science. Due to these new and emerging challenges in scientific imaging, ImageJ is at a critical development crossroads. We present ImageJ2, a total redesign of ImageJ offering a host of new functionality. It separates concerns, fully decoupling the data model from the user interface. It emphasizes integration with external applications to maximize interoperability. Its robust new plugin framework allows everything from image formats, to scripting languages, to visualization to be extended by the community. The redesigned data model supports arbitrarily large, N-dimensional datasets, which are increasingly common in modern image acquisition. Despite the scope of these changes, backwards compatibility is maintained such that this new functionality can be seamlessly integrated with the classic ImageJ interface, allowing users and developers to migrate to these new methods at their own pace. ImageJ2 provides a framework engineered for flexibility, intended to support these requirements as well as accommodate future needs

    Digital Ecosystems: Ecosystem-Oriented Architectures

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    We view Digital Ecosystems to be the digital counterparts of biological ecosystems. Here, we are concerned with the creation of these Digital Ecosystems, exploiting the self-organising properties of biological ecosystems to evolve high-level software applications. Therefore, we created the Digital Ecosystem, a novel optimisation technique inspired by biological ecosystems, where the optimisation works at two levels: a first optimisation, migration of agents which are distributed in a decentralised peer-to-peer network, operating continuously in time; this process feeds a second optimisation based on evolutionary computing that operates locally on single peers and is aimed at finding solutions to satisfy locally relevant constraints. The Digital Ecosystem was then measured experimentally through simulations, with measures originating from theoretical ecology, evaluating its likeness to biological ecosystems. This included its responsiveness to requests for applications from the user base, as a measure of the ecological succession (ecosystem maturity). Overall, we have advanced the understanding of Digital Ecosystems, creating Ecosystem-Oriented Architectures where the word ecosystem is more than just a metaphor.Comment: 39 pages, 26 figures, journa

    Does Form follow Function? Connecting Function Modelling and Geometry Modelling for Design Space Exploration

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    The aerospace industry, representative of industries developing complex products, faces challenges from changes in user behaviour, legislation, environmental policy. Meeting these challenges will require the development of radically new products. Radically new technologies and solutions need to be explored, investigated, and integrated into existing aerospace component architectures. The currently available design space exploration (DSE) methods, mainly based around computer-aided design (CAD) modelling, do not provide sufficient support for this exploration. These methods often lack a representation of the product’s architecture in relation to its design rationale (DR)—they do not illustrate how form follows function. Hence, relations between different functions and solutions, as well as how novel ideas relate to the legacy design, are not captured. In particular, the connection between a product’s function and the embodiment of its solution is not captured in the applied product modelling approaches, and can therefore not be used in the product development process.To alleviate this situation, this thesis presents a combined function and geometry-modelling approach with automated generation of CAD models for variant concepts. The approach builds on enhanced function means (EF-M) modelling for representation of the design space and the legacy design’s position in it. EF-M is also used to capture novel design solutions and reference them to the legacy design’s architecture. A design automation (DA) approach based on modularisation of the CAD model, which in turn is based on the functional decomposition of the product concepts, is used to capture geometric product information. A combined function-geometry object model captures the relations between functions, solutions, and geometry. This allows for CAD models of concepts based on alternative solutions to be generated.The function- and geometry-exploration (FGE) approach has been developed and tested in collaboration with an aerospace manufacturing company. A proof-of-concept tool implementing the approach has been realised. The approach has been validated for decomposition, innovation, and embodiment of new concepts in multiple studies involving three different aerospace suppliers. Application of FGE provides knowledge capture and representation, connecting the teleological and geometric aspects of the product. Furthermore, it supports the exploration of increasingly novel solutions, enabling the coverage of a wider area of the design space.The connection between the modelling domains addresses a research gap for the “integration of function architectures with CAD models”.While the FGE approach has been tested in laboratory environments as well as in applied product development projects, further development is needed to refine CAD integration and user experience and integrate additional modelling domains
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