24 research outputs found
Investigating non-classical correlations between decision fused multi-modal documents
Correlation has been widely used to facilitate various information retrieval methods such as query expansion, relevance feedback, document clustering, and multi-modal fusion. Especially, correlation and independence are important issues when fusing different modalities that influence a multi-modal information retrieval process. The basic idea of correlation is that an observable can help predict or enhance another observable. In quantum mechanics, quantum correlation, called entanglement, is a sort of correlation between the observables measured in atomic-size particles when these particles are not necessarily collected in ensembles. In this paper, we examine a multimodal fusion scenario that might be similar to that encountered in physics by firstly measuring two observables (i.e., text-based relevance and image-based relevance) of a multi-modal document without counting on an ensemble of multi-modal documents already labeled in terms of these two variables. Then, we investigate the existence of non-classical correlations between pairs of multi-modal documents. Despite there are some basic differences between entanglement and classical correlation encountered in the macroscopic world, we investigate the existence of this kind of non-classical correlation through the Bell inequality violation. Here, we experimentally test several novel association methods in a small-scale experiment. However, in the current experiment we did not find any violation of the Bell inequality. Finally, we present a series of interesting discussions, which may provide theoretical and empirical insights and inspirations for future development of this direction
Contextual Query Using Bell Tests
Tests are essential in Information Retrieval and Data Mining in order to
evaluate the effectiveness of a query. An automatic measure tool intended to
exhibit the meaning of words in context has been developed and linked with
Quantum Theory, particularly entanglement. "Quantum like" experiments were
undertaken on semantic space based on the Hyperspace Analogue Language (HAL)
method. A quantum HAL model was implemented using state vectors issued from the
HAL matrix and query observables, testing a wide range of windows sizes. The
Bell parameter S, associating measures on two words in a document, was derived
showing peaks for specific window sizes. The peaks show maximum quantum
violation of the Bell inequalities and are document dependent. This new
correlation measure inspired by Quantum Theory could be promising for measuring
query relevance.Comment: 12 pages, 3 figure
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Towards an empirical test of realism in cognition
We review recent progress in designing an empirical test of (temporal) realism in cognition. Realism in this context is the property that cognitive variables always have well defined (if possibly unknown) values at all times. We focus most of our attention in this contribution on discussing the exact notion of realism that is to be tested, as we feel this issue has not received enough attention to date. We also give a brief outline of the empirical test, including some comments on an experimental realisation, and we discuss what we should conclude from any purported experimental ‘disproof’ of realism. This contribution is based on Yearsley and Pothos (2014)
Episodic Source Memory over Distribution by Quantum-Like Dynamics – A Model Exploration
In source memory studies, a decision-maker is concerned with identifying the context in which a given episodic experience occurred. A common paradigm for studying source memory is the ‘three-list’ experimental paradigm, where a subject studies three lists of words and is later asked whether a given word appeared on one or more of the studied lists. Surprisingly, the sum total of the acceptance probabilities generated by asking for the source of a word separately for each list (‘list 1?’, ‘list 2?’, ‘list 3?’) exceeds the acceptance probability generated by asking whether that word occurred on the union of the lists (‘list 1 or 2 or 3?’). The episodic memory for a given word therefore appears over distributed on the disjoint contexts of the lists. A quantum episodic memory model [QEM] was proposed by Brainerd, Wang and Reyna [8] to explain this type of result. In this paper, we apply a Hamiltonian dynamical extension of QEM for over distribution of source memory. The Hamiltonian operators are simultaneously driven by parameters for re-allocation of gist-based and verbatim-based acceptance support as subjects are exposed to the cue word in the first temporal stage, and are attenuated for description-dependence by the querying probe in the second temporal stage. Overall, the model predicts well the choice proportions in both separate list and union list queries and the over distribution effect, suggesting that a Hamiltonian dynamics for QEM can provide a good account of the acceptance processes involved in episodic memory tasks
Moral Dilemmas for Artificial Intelligence: a position paper on an application of Compositional Quantum Cognition
Traditionally, the way one evaluates the performance of an Artificial
Intelligence (AI) system is via a comparison to human performance in specific
tasks, treating humans as a reference for high-level cognition. However, these
comparisons leave out important features of human intelligence: the capability
to transfer knowledge and make complex decisions based on emotional and
rational reasoning. These decisions are influenced by current inferences as
well as prior experiences, making the decision process strongly subjective and
apparently biased. In this context, a definition of compositional intelligence
is necessary to incorporate these features in future AI tests. Here, a concrete
implementation of this will be suggested, using recent developments in quantum
cognition, natural language and compositional meaning of sentences, thanks to
categorical compositional models of meaning.Comment: 15 pages, 3 figures, Conference paper at Quantum Interaction 2018,
Nice, France. Published in Lecture Notes in Computer Science, vol 11690,
Springer, Cham. Online ISBN 978-3-030-35895-
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Ontology-based end-user visual query formulation: Why, what, who, how, and which?
Value creation in an organisation is a time-sensitive and data-intensive process, yet it is often delayed and bounded by the reliance on IT experts extracting data for domain experts. Hence, there is a need for providing people who are not professional developers with the flexibility to pose relatively complex and ad hoc queries in an easy and intuitive way. In this respect, visual methods for query formulation undertake the challenge of making querying independent of users’ technical skills and the knowledge of the underlying textual query language and the structure of data. An ontology is more promising than the logical schema of the underlying data for guiding users in formulating queries, since it provides a richer vocabulary closer to the users’ understanding. However, on the one hand, today the most of world’s enterprise data reside in relational databases rather than triple stores, and on the other, visual query formulation has become more compelling due to ever-increasing data size and complexity—known as Big Data. This article presents and argues for ontology-based visual query formulation for end-users; discusses its feasibility in terms of ontology-based data access, which virtualises legacy relational databases as RDF, and the dimensions of Big Data; presents key conceptual aspects and dimensions, challenges, and requirements; and reviews, categorises, and discusses notable approaches and systems
Investigating Aboutness Axioms using Information Fields
This article proposes a framework, a so called information field, which allows information retrieval mechanisms to be compared inductively instead of experimentally. Such a comparison occurs as follows: Both retrieval mechanisms are first mapped to an associated information field. Within the field, the axioms that drive the retrieval process can be filtered out. In this way, the implicit assumptions governing an information retrieval mechanism can be brought to light. The retrieval mechanisms can then be compared according to which axioms they are governed by. Using this method it is shown that Boolean retrieval is more powerful than a strict form of coordinate retrieval. The salient point is not this result in itself, but how the result was achieved. 1 Introduction The logic based approach to information retrieval has been around for some time now. So far, a number of inference mechanisms, both strict and plausible, have been proposed for driving the retrieval process [15, 6, 4, 12]...
Probabilistic programs for investigating contextuality in human information processing
This article presents a framework for analysing contextuality in human information processing. In the quantum cognition community there has been ongoing speculation that something-like quantum contextuality may be present in human cognition. The framework aims to pro- vide a convenient means of designing experiments and performing contextuality analysis in order to ascertain whether this speculation holds. Experimental designs are expressed as probabilistic programs. The semantics a program are composed from hypergraphs called contextuality scenarios, which, in turn, are used to determine whether the phenomenon being studied is contextual. Examples are provided illustrate the frame- work as well as some reflection about its broader application to quantum physics