7,042 research outputs found
A Comparative Study of Reservoir Computing for Temporal Signal Processing
Reservoir computing (RC) is a novel approach to time series prediction using
recurrent neural networks. In RC, an input signal perturbs the intrinsic
dynamics of a medium called a reservoir. A readout layer is then trained to
reconstruct a target output from the reservoir's state. The multitude of RC
architectures and evaluation metrics poses a challenge to both practitioners
and theorists who study the task-solving performance and computational power of
RC. In addition, in contrast to traditional computation models, the reservoir
is a dynamical system in which computation and memory are inseparable, and
therefore hard to analyze. Here, we compare echo state networks (ESN), a
popular RC architecture, with tapped-delay lines (DL) and nonlinear
autoregressive exogenous (NARX) networks, which we use to model systems with
limited computation and limited memory respectively. We compare the performance
of the three systems while computing three common benchmark time series:
H{\'e}non Map, NARMA10, and NARMA20. We find that the role of the reservoir in
the reservoir computing paradigm goes beyond providing a memory of the past
inputs. The DL and the NARX network have higher memorization capability, but
fall short of the generalization power of the ESN
Empirical Methodology for Crowdsourcing Ground Truth
The process of gathering ground truth data through human annotation is a
major bottleneck in the use of information extraction methods for populating
the Semantic Web. Crowdsourcing-based approaches are gaining popularity in the
attempt to solve the issues related to volume of data and lack of annotators.
Typically these practices use inter-annotator agreement as a measure of
quality. However, in many domains, such as event detection, there is ambiguity
in the data, as well as a multitude of perspectives of the information
examples. We present an empirically derived methodology for efficiently
gathering of ground truth data in a diverse set of use cases covering a variety
of domains and annotation tasks. Central to our approach is the use of
CrowdTruth metrics that capture inter-annotator disagreement. We show that
measuring disagreement is essential for acquiring a high quality ground truth.
We achieve this by comparing the quality of the data aggregated with CrowdTruth
metrics with majority vote, over a set of diverse crowdsourcing tasks: Medical
Relation Extraction, Twitter Event Identification, News Event Extraction and
Sound Interpretation. We also show that an increased number of crowd workers
leads to growth and stabilization in the quality of annotations, going against
the usual practice of employing a small number of annotators.Comment: in publication at the Semantic Web Journa
Cognitive Interpretation of Everyday Activities - Toward Perceptual Narrative Based Visuo-Spatial Scene Interpretation
We position a narrative-centred computational model for high-level knowledge representation and reasoning in the context of a range of assistive technologies concerned with visuo-spatial perception and cognition tasks. Our proposed narrative model encompasses aspects such as space, events, actions, change, and interaction from the viewpoint of commonsense reasoning and learning in large-scale cognitive systems. The broad focus of this paper is on the domain of human-activity interpretation in smart environments, ambient intelligence etc. In the backdrop of a smart meeting cinematography domain, we position the proposed narrative model, preliminary work on perceptual narrativisation, and the immediate outlook on constructing general-purpose open-source tools for perceptual narrativisation
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