33,396 research outputs found
An Investigation into the Pedagogical Features of Documents
Characterizing the content of a technical document in terms of its learning
utility can be useful for applications related to education, such as generating
reading lists from large collections of documents. We refer to this learning
utility as the "pedagogical value" of the document to the learner. While
pedagogical value is an important concept that has been studied extensively
within the education domain, there has been little work exploring it from a
computational, i.e., natural language processing (NLP), perspective. To allow a
computational exploration of this concept, we introduce the notion of
"pedagogical roles" of documents (e.g., Tutorial and Survey) as an intermediary
component for the study of pedagogical value. Given the lack of available
corpora for our exploration, we create the first annotated corpus of
pedagogical roles and use it to test baseline techniques for automatic
prediction of such roles.Comment: 12th Workshop on Innovative Use of NLP for Building Educational
Applications (BEA) at EMNLP 2017; 12 page
Training Process Reduction Based On Potential Weights Linear Analysis To Accelarate Back Propagation Network
Learning is the important property of Back Propagation Network (BPN) and
finding the suitable weights and thresholds during training in order to improve
training time as well as achieve high accuracy. Currently, data pre-processing
such as dimension reduction input values and pre-training are the contributing
factors in developing efficient techniques for reducing training time with high
accuracy and initialization of the weights is the important issue which is
random and creates paradox, and leads to low accuracy with high training time.
One good data preprocessing technique for accelerating BPN classification is
dimension reduction technique but it has problem of missing data. In this
paper, we study current pre-training techniques and new preprocessing technique
called Potential Weight Linear Analysis (PWLA) which combines normalization,
dimension reduction input values and pre-training. In PWLA, the first data
preprocessing is performed for generating normalized input values and then
applying them by pre-training technique in order to obtain the potential
weights. After these phases, dimension of input values matrix will be reduced
by using real potential weights. For experiment results XOR problem and three
datasets, which are SPECT Heart, SPECTF Heart and Liver disorders (BUPA) will
be evaluated. Our results, however, will show that the new technique of PWLA
will change BPN to new Supervised Multi Layer Feed Forward Neural Network
(SMFFNN) model with high accuracy in one epoch without training cycle. Also
PWLA will be able to have power of non linear supervised and unsupervised
dimension reduction property for applying by other supervised multi layer feed
forward neural network model in future work.Comment: 11 pages IEEE format, International Journal of Computer Science and
Information Security, IJCSIS 2009, ISSN 1947 5500, Impact factor 0.42
Multilabel Classification with R Package mlr
We implemented several multilabel classification algorithms in the machine
learning package mlr. The implemented methods are binary relevance, classifier
chains, nested stacking, dependent binary relevance and stacking, which can be
used with any base learner that is accessible in mlr. Moreover, there is access
to the multilabel classification versions of randomForestSRC and rFerns. All
these methods can be easily compared by different implemented multilabel
performance measures and resampling methods in the standardized mlr framework.
In a benchmark experiment with several multilabel datasets, the performance of
the different methods is evaluated.Comment: 18 pages, 2 figures, to be published in R Journal; reference
correcte
Data-driven design of intelligent wireless networks: an overview and tutorial
Data science or "data-driven research" is a research approach that uses real-life data to gain insight about the behavior of systems. It enables the analysis of small, simple as well as large and more complex systems in order to assess whether they function according to the intended design and as seen in simulation. Data science approaches have been successfully applied to analyze networked interactions in several research areas such as large-scale social networks, advanced business and healthcare processes. Wireless networks can exhibit unpredictable interactions between algorithms from multiple protocol layers, interactions between multiple devices, and hardware specific influences. These interactions can lead to a difference between real-world functioning and design time functioning. Data science methods can help to detect the actual behavior and possibly help to correct it. Data science is increasingly used in wireless research. To support data-driven research in wireless networks, this paper illustrates the step-by-step methodology that has to be applied to extract knowledge from raw data traces. To this end, the paper (i) clarifies when, why and how to use data science in wireless network research; (ii) provides a generic framework for applying data science in wireless networks; (iii) gives an overview of existing research papers that utilized data science approaches in wireless networks; (iv) illustrates the overall knowledge discovery process through an extensive example in which device types are identified based on their traffic patterns; (v) provides the reader the necessary datasets and scripts to go through the tutorial steps themselves
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PetroPlot: A plotting and data management tool set for Microsoft Excel
PetroPlot is a 4000-line software code written in Visual Basic for the spreadsheet program Excel that automates plotting and data management tasks for large amount of data. The major plotting functions include: automation of large numbers of multiseries XY plots; normalized diagrams (e.g., spider diagrams); replotting of any complex formatted diagram with multiple series for any other axis parameters; addition of customized labels for individual data points; and labeling flexible log scale axes. Other functions include: assignment of groups for samples based on multiple customized criteria; removal of nonnumeric values; calculation of averages/standard deviations; calculation of correlation matrices; deletion of nonconsecutive rows; and compilation of multiple rows of data for a single sample to single rows appropriate for plotting. A cubic spline function permits curve fitting to complex time series, and comparison of data to the fits. For users of Excel, PetroPlot increases efficiency of data manipulation and visualization by orders of magnitude and allows exploration of large data sets that would not be possible making plots individually. The source codes are open to all users
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