11,867 research outputs found
Get the gist? The effects of processing depth on false recognition in short-term and long-term memory
Gist-based processing has been proposed to account for robust false memories in the converging-associates task. The deep-encoding processes known to enhance verbatim memory also strengthen gist memory and increase distortions of long-term memory (LTM). Recent research has demonstrated that compelling false memory illusions are relatively delay-invariant, also occurring under canonical short-term memory (STM) conditions. To investigate the contributions of gist to false memory at short and long delays, processing depth was manipulated as participants encoded lists of four semantically related words and were probed immediately, following a filled 3- to 4-s retention interval, or approximately 20 min later, in a surprise recognition test. In two experiments, the encoding manipulation dissociated STM and LTM on the frequency, but not the phenomenology, of false memory. Deep encoding at STM increases false recognition rates at LTM, but confidence ratings and remember/know judgments are similar across delays and do not differ as a function of processing depth. These results suggest that some shared and some unique processes underlie false memory illusions at short and long delays
Iris Codes Classification Using Discriminant and Witness Directions
The main topic discussed in this paper is how to use intelligence for
biometric decision defuzzification. A neural training model is proposed and
tested here as a possible solution for dealing with natural fuzzification that
appears between the intra- and inter-class distribution of scores computed
during iris recognition tests. It is shown here that the use of proposed neural
network support leads to an improvement in the artificial perception of the
separation between the intra- and inter-class score distributions by moving
them away from each other.Comment: 6 pages, 5 figures, Proc. 5th IEEE Int. Symp. on Computational
Intelligence and Intelligent Informatics (Floriana, Malta, September 15-17),
ISBN: 978-1-4577-1861-8 (electronic), 978-1-4577-1860-1 (print
On fuzzy-qualitative descriptions and entropy
This paper models the assessments of a group of experts when evaluating different magnitudes, features or objects by using linguistic descriptions. A new general representation of linguistic descriptions is provided by unifying ordinal and fuzzy perspectives. Fuzzy qualitative labels are proposed as a generalization of the concept of qualitative labels over a well-ordered set. A lattice structure is established in the set of fuzzy-qualitative labels to enable the introduction of fuzzy-qualitative descriptions as L-fuzzy sets. A theorem is given that characterizes finite fuzzy partitions using fuzzy-qualitative labels, the cores and supports of which are qualitative labels. This theorem leads to a mathematical justification for commonly-used fuzzy partitions of real intervals via trapezoidal fuzzy sets. The information of a fuzzy-qualitative label is defined using a measure of specificity, in order to introduce the entropy of fuzzy-qualitative descriptions. (C) 2016 Elsevier Inc. All rights reserved.Peer ReviewedPostprint (author's final draft
Unsupervised Heart-rate Estimation in Wearables With Liquid States and A Probabilistic Readout
Heart-rate estimation is a fundamental feature of modern wearable devices. In
this paper we propose a machine intelligent approach for heart-rate estimation
from electrocardiogram (ECG) data collected using wearable devices. The novelty
of our approach lies in (1) encoding spatio-temporal properties of ECG signals
directly into spike train and using this to excite recurrently connected
spiking neurons in a Liquid State Machine computation model; (2) a novel
learning algorithm; and (3) an intelligently designed unsupervised readout
based on Fuzzy c-Means clustering of spike responses from a subset of neurons
(Liquid states), selected using particle swarm optimization. Our approach
differs from existing works by learning directly from ECG signals (allowing
personalization), without requiring costly data annotations. Additionally, our
approach can be easily implemented on state-of-the-art spiking-based
neuromorphic systems, offering high accuracy, yet significantly low energy
footprint, leading to an extended battery life of wearable devices. We
validated our approach with CARLsim, a GPU accelerated spiking neural network
simulator modeling Izhikevich spiking neurons with Spike Timing Dependent
Plasticity (STDP) and homeostatic scaling. A range of subjects are considered
from in-house clinical trials and public ECG databases. Results show high
accuracy and low energy footprint in heart-rate estimation across subjects with
and without cardiac irregularities, signifying the strong potential of this
approach to be integrated in future wearable devices.Comment: 51 pages, 12 figures, 6 tables, 95 references. Under submission at
Elsevier Neural Network
Functional Dynamics I : Articulation Process
The articulation process of dynamical networks is studied with a functional
map, a minimal model for the dynamic change of relationships through iteration.
The model is a dynamical system of a function , not of variables, having a
self-reference term , introduced by recalling that operation in a
biological system is often applied to itself, as is typically seen in rules in
the natural language or genes. Starting from an inarticulate network, two types
of fixed points are formed as an invariant structure with iterations. The
function is folded with time, until it has finite or infinite piecewise-flat
segments of fixed points, regarded as articulation. For an initial logistic
map, attracted functions are classified into step, folded step, fractal, and
random phases, according to the degree of folding. Oscillatory dynamics are
also found, where function values are mapped to several fixed points
periodically. The significance of our results to prototype categorization in
language is discussed.Comment: 48 pages, 15 figeres (5 gif files
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