94,849 research outputs found
Online Tool Condition Monitoring Based on Parsimonious Ensemble+
Accurate diagnosis of tool wear in metal turning process remains an open
challenge for both scientists and industrial practitioners because of
inhomogeneities in workpiece material, nonstationary machining settings to suit
production requirements, and nonlinear relations between measured variables and
tool wear. Common methodologies for tool condition monitoring still rely on
batch approaches which cannot cope with a fast sampling rate of metal cutting
process. Furthermore they require a retraining process to be completed from
scratch when dealing with a new set of machining parameters. This paper
presents an online tool condition monitoring approach based on Parsimonious
Ensemble+, pENsemble+. The unique feature of pENsemble+ lies in its highly
flexible principle where both ensemble structure and base-classifier structure
can automatically grow and shrink on the fly based on the characteristics of
data streams. Moreover, the online feature selection scenario is integrated to
actively sample relevant input attributes. The paper presents advancement of a
newly developed ensemble learning algorithm, pENsemble+, where online active
learning scenario is incorporated to reduce operator labelling effort. The
ensemble merging scenario is proposed which allows reduction of ensemble
complexity while retaining its diversity. Experimental studies utilising
real-world manufacturing data streams and comparisons with well known
algorithms were carried out. Furthermore, the efficacy of pENsemble was
examined using benchmark concept drift data streams. It has been found that
pENsemble+ incurs low structural complexity and results in a significant
reduction of operator labelling effort.Comment: this paper has been published by IEEE Transactions on Cybernetic
Information Extraction, Data Integration, and Uncertain Data Management: The State of The Art
Information Extraction, data Integration, and uncertain data management are different areas of research that got vast focus in the last two decades. Many researches tackled those areas of research individually. However, information extraction systems should have integrated with data integration methods to make use of the extracted information. Handling uncertainty in extraction and integration process is an important issue to enhance the quality of the data in such integrated systems. This article presents the state of the art of the mentioned areas of research and shows the common grounds and how to integrate information extraction and data integration under uncertainty management cover
Neural activity associated with the passive prediction of ambiguity and risk for aversive events
In economic decision making, outcomes are described in terms of risk (uncertain outcomes with certain probabilities) and ambiguity
(uncertain outcomes with uncertain probabilities). Humans are more averse to ambiguity than to risk, with a distinct neural system
suggested as mediating this effect. However, there has been no clear disambiguation of activity related to decisions themselves from
perceptual processing of ambiguity. In a functional magnetic resonance imaging (fMRI) experiment, we contrasted ambiguity, defined as
a lack of information about outcome probabilities, to risk, where outcome probabilities are known, or ignorance, where outcomes are
completely unknown and unknowable.Wemodified previously learned pavlovian CSstimuli such that they became an ambiguous cue
and contrasted evoked brain activity both with an unmodified predictive CS(risky cue), and a cue that conveyed no information about
outcome probabilities (ignorance cue). Compared with risk, ambiguous cues elicited activity in posterior inferior frontal gyrus and
posterior parietal cortex during outcome anticipation. Furthermore, a similar set of regions was activated when ambiguous cues were
compared with ignorance cues. Thus, regions previously shown to be engaged by decisions about ambiguous rewarding outcomes are
also engaged by ambiguous outcome prediction in the context of aversive outcomes. Moreover, activation in these regions was seen even
when no actual decision is made. Our findings suggest that these regions subserve a general function of contextual analysis when search
for hidden information during outcome anticipation is both necessary and meaningful
An Incremental Construction of Deep Neuro Fuzzy System for Continual Learning of Non-stationary Data Streams
Existing FNNs are mostly developed under a shallow network configuration
having lower generalization power than those of deep structures. This paper
proposes a novel self-organizing deep FNN, namely DEVFNN. Fuzzy rules can be
automatically extracted from data streams or removed if they play limited role
during their lifespan. The structure of the network can be deepened on demand
by stacking additional layers using a drift detection method which not only
detects the covariate drift, variations of input space, but also accurately
identifies the real drift, dynamic changes of both feature space and target
space. DEVFNN is developed under the stacked generalization principle via the
feature augmentation concept where a recently developed algorithm, namely
gClass, drives the hidden layer. It is equipped by an automatic feature
selection method which controls activation and deactivation of input attributes
to induce varying subsets of input features. A deep network simplification
procedure is put forward using the concept of hidden layer merging to prevent
uncontrollable growth of dimensionality of input space due to the nature of
feature augmentation approach in building a deep network structure. DEVFNN
works in the sample-wise fashion and is compatible for data stream
applications. The efficacy of DEVFNN has been thoroughly evaluated using seven
datasets with non-stationary properties under the prequential test-then-train
protocol. It has been compared with four popular continual learning algorithms
and its shallow counterpart where DEVFNN demonstrates improvement of
classification accuracy. Moreover, it is also shown that the concept drift
detection method is an effective tool to control the depth of network structure
while the hidden layer merging scenario is capable of simplifying the network
complexity of a deep network with negligible compromise of generalization
performance.Comment: This paper has been published in IEEE Transactions on Fuzzy System
Augmenting Deep Learning Performance in an Evidential Multiple Classifier System
International audienceThe main objective of this work is to study the applicability of ensemble methods in the context of deep learning with limited amounts of labeled data. We exploit an ensemble of neural networks derived using Monte Carlo dropout, along with an ensemble of SVM classifiers which owes its effectiveness to the hand-crafted features used as inputs and to an active learning procedure. In order to leverage each classifier's respective strengths, we combine them in an evidential framework, which models specifically their imprecision and uncertainty. The application we consider in order to illustrate the interest of our Multiple Classifier System is pedestrian detection in high-density crowds, which is ideally suited for its difficulty, cost of labeling and intrinsic imprecision of annotation data. We show that the fusion resulting from the effective modeling of uncertainty allows for performance improvement, and at the same time, for a deeper interpretation of the result in terms of commitment of the decision
Research on learning: Potential for improving college ecology teaching
Provides pedagogical insight concerning learners' pre-conceptions and misconceptions about ecology The resource being annotated is: http://www.dlese.org/dds/catalog_DLESE-000-000-003-202.htm
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Issues of quality assurance in the management of plagiarism in blended learning environments
Increasing access to and availability of electronic resources presents students with a rich
library of opportunities for independent study. But students also find themselves in the
confusing territory of how they should best use these resources within their assessment
activities. Likewise, teaching institutions are faced with the problems of plagiarism and
collusion, and the challenges of educating, deterring, detecting, and dealing with breaches of
policy in a fair and consistent way across all disciplines.
This paper examines issues of quality assurance in the management of plagiarism by
discussing the following questions:
â How can effective automated plagiarism detection services be introduced and managed
across the institution?
â What teaching and assessment practices can be adopted to deter plagiarism?
â What part should collusion and plagiarism detection tools play in educating and deterring
students?
â What are appropriate penalties for plagiarism and collusion and how can these be
applied consistently across disciplines?
Drawing together three distinct strands of research, in both distance and campus based
institutions, the authors discuss how practice and policy have evolved in recent years in an
attempt to reduce the incidence of plagiarism and collusion. The paper will illustrate this
evolution by reporting on recent developments in assessment strategy, detection tools, and
policy within two UK HE Institutions: The UK Open University and Manchester Metropolitan
University
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