Trustable service discovery for highly dynamic decentralized workflows

Barclay, Iain; Simpkin, Chris; Bent, Graham; Porta, Thomas La; Millar, D. A.; Preece, Alun; Taylor, Ian; Verma, Dinesh

doi:10.1016/j.future.2022.03.035

Cited by 2 publications

(2 citation statements)

References 43 publications

(65 reference statements)

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“…In [384], the approach was implemented in a particular workflow system: Node-RED. In [8], the approach was extended further to take into account the level of trust associated with various elements when selecting services.…”

Section: Transmission Of Data Structuresmentioning

confidence: 99%

A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part II: Applications, Cognitive Models, and Challenges

et al. 2023

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This is Part II of the two-part comprehensive survey devoted to a computing framework most commonly known under the names Hyperdimensional Computing and Vector Symbolic Architectures (HDC/VSA). Both names refer to a family of computational models that use high-dimensional distributed representations and rely on the algebraic properties of their key operations to incorporate the advantages of structured symbolic representations and vector distributed representations. Holographic Reduced Representations [322, 327] is an influential HDC/VSA model that is well-known in the machine learning domain and often used to refer to the whole family. However, for the sake of consistency, we use HDC/VSA to refer to the field. Part I of this survey [223] covered foundational aspects of the field, such as the historical context leading to the development of HDC/VSA, key elements of any HDC/VSA model, known HDC/VSA models, and the transformation of input data of various types into high-dimensional vectors suitable for HDC/VSA. This second part surveys existing applications, the role of HDC/VSA in cognitive computing and architectures, as well as directions for future work. Most of the applications lie within the Machine Learning/Artificial Intelligence domain, however, we also cover other applications to provide a complete picture. The survey is written to be useful for both newcomers and practitioners.

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Section: Transmission Of Data Structuresmentioning

confidence: 99%

A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part II: Applications, Cognitive Models, and Challenges

et al. 2023

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“…5). We note the use of the method of receptive fields for continuous optimization [55,56] and the method based on random projection in problems of decentralized flows [57].…”

Section: Input Data Transformationsmentioning

confidence: 99%

Neural Distributed Representations for Artificial Intelligence and Modeling of Thinking

Rachkovskij¹,

Gritsenko²,

Volkov³

et al. 2022

Kibern. vyčisl. teh.

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coming these shortcomings is a necessary condition for advancing from specialized Artificial Intelligence to general one, which requires the development of alternative approaches.The purpose of the paper is to present an overview of research in this direction, which has been carried out at the International Center for 25 years. The approach being developed stems from the ideas of N. M. Amosov and his scientific school. Connections to the Hyperdimensional Computing (HDC) and Vector Symbolic Architectures (VSA) field as well as to current brain research are also provided.Results. The concept of distributed data representation is outlined, including HDC/VSA that are capable of representing various data structures. The developed paradigm of Associative-Projective Neural Networks is considered: codevector representation of data, superposition and binding operations, general architecture, transformation of data of various types into codevectors, methods for solving problems and applications. Conclusion. An adequate representation of data is one of the key issues within the Artificial Intelligence. The main area of research reviewed in this article is the problem of representing heterogeneous data in Artificial Intelligence systems in a unified format based on modeling the neural organization of the brain and the mechanisms of thinking. The approach under development is based on the hypothesis of distributed representation of information in the brain and allows representing various types of data, from numeric values to graphs, as vectors of large but fixed dimensionality.The most important advantages of the developed approach are the possibility of natural integration and efficient processing of various types of data and knowledge, a high degree of parallel computing, reliability and resistance to noise, the possibility of hardware implementation with high performance and energy efficiency, data processing based on associative similarity search -similar to how human memory works. This allows one to unify the methods, algorithms, and software and hardware for Artificial Intelligence systems, increase their scalability in terms of speed and memory with an increase in data volume and complexity.The research creates the basis for overcoming the shortcomings of current approaches to the specialized Artificial Intelligence based on Deep Neural Networks and paves the way for the creation of Artificial General Intelligence.

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Trustable service discovery for highly dynamic decentralized workflows

Cited by 2 publications

References 43 publications

A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part II: Applications, Cognitive Models, and Challenges

A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part II: Applications, Cognitive Models, and Challenges

Neural Distributed Representations for Artificial Intelligence and Modeling of Thinking

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