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Jackendoff (2002) posed four challenges that linguistic combinatoriality and rules of language present to theories of brain function. The essence of these problems is the question of how to neurally instantiate the rapid construction and transformation of the compositional structures that are typically taken to be the domain of symbolic processing. He(More)
This paper describes a rapid technique: communal analysis suspicion scoring (CASS), for generating numeric suspicion scores on streaming credit applications based on implicit links to each other, over both time and space. CASS includes pair-wise communal scoring of identifier attributes for applications, definition of categories of suspiciousness for(More)
Entity resolution, also known as data matching or record linkage, is the task of identifying and matching records from several databases that refer to the same entities. Traditionally, entity resolution has been applied in batch-mode and on static databases. However, many organisations are increasingly faced with the challenge of having large databases(More)
Most research into entity resolution (also known as record linkage or data matching) has concentrated on the quality of the matching results. In this paper, we focus on matching time and scalability, with the aim to achieve large-scale real-time entity resolution. Traditional entity resolution techniques have assumed the matching of two static databases. In(More)
We provide an overview of Vector Symbolic Architectures (VSA), a class of structured associative memory models that offers a number of desirable features for artificial general intelligence. By directly encoding structure using familiar , computationally efficient algorithms, VSA bypasses many of the problems that have consumed unnecessary effort and(More)
—Identity crime is well known, prevalent, and costly; and credit application fraud is a specific case of identity crime. The existing non-data mining detection systems of business rules and scorecards, and known fraud matching have limitations. To address these limitations and combat identity crime in real-time, this paper proposes a new multi-layered(More)
— Analogy-making is a key function of human cog-nition. Therefore, the development of computational models of analogy that automatically learn from examples can lead to significant advances in cognitive systems. Analogies require complex, relational representations of learned structures, which is challenging for both symbolic and neurally inspired models.(More)
In this position paper we argue that BICA must simultaneously be compatible with the explanation of human cognition and support the human design of artificial cognitive systems. Most cognitive neuroscience models fail to provide a basis for implementation because they neglect necessary levels of functional organisation in jumping directly from physical(More)
Entity resolution is the process of identifying groups of records in a single or multiple data sources that represent the same real-world entity. It is an important tool in data de-duplication, in linking records across databases, and in matching query records against a database of existing entities. Most existing entity resolution techniques complete the(More)