1,482 research outputs found
ResumeNet: A Learning-based Framework for Automatic Resume Quality Assessment
Recruitment of appropriate people for certain positions is critical for any
companies or organizations. Manually screening to select appropriate candidates
from large amounts of resumes can be exhausted and time-consuming. However,
there is no public tool that can be directly used for automatic resume quality
assessment (RQA). This motivates us to develop a method for automatic RQA.
Since there is also no public dataset for model training and evaluation, we
build a dataset for RQA by collecting around 10K resumes, which are provided by
a private resume management company. By investigating the dataset, we identify
some factors or features that could be useful to discriminate good resumes from
bad ones, e.g., the consistency between different parts of a resume. Then a
neural-network model is designed to predict the quality of each resume, where
some text processing techniques are incorporated. To deal with the label
deficiency issue in the dataset, we propose several variants of the model by
either utilizing the pair/triplet-based loss, or introducing some
semi-supervised learning technique to make use of the abundant unlabeled data.
Both the presented baseline model and its variants are general and easy to
implement. Various popular criteria including the receiver operating
characteristic (ROC) curve, F-measure and ranking-based average precision (AP)
are adopted for model evaluation. We compare the different variants with our
baseline model. Since there is no public algorithm for RQA, we further compare
our results with those obtained from a website that can score a resume.
Experimental results in terms of different criteria demonstrate the
effectiveness of the proposed method. We foresee that our approach would
transform the way of future human resources management.Comment: ICD
Projection-based reduced order modeling and data-driven artificial viscosity closures for incompressible fluid flows
Projection-based reduced order models rely on offline-online model
decomposition, where the data-based energetic spatial basis is used in the
expensive offline stage to obtain equations of reduced states that evolve in
time during the inexpensive online stage. The online stage requires a solution
method for the dynamic evolution of the coupled system of pressure and velocity
states for incompressible fluid flows. The first contribution of this article
is to demonstrate the applicability of the incremental pressure correction
scheme for the dynamic evolution of pressure and velocity states. The evolution
of a large number of these reduced states in the online stage can be expensive.
In contrast, the accuracy significantly decreases if only a few reduced states
are considered while not accounting for the interactions between unresolved and
resolved states. The second contribution of this article is to compare three
closure model forms based on global, modal and tensor artificial viscosity
approximation to account for these interactions. The unknown model parameters
are determined using two calibration techniques: least squares minimization of
error in energy approximation and closure term approximation. This article
demonstrates that an appropriate selection of solution methods and data-driven
artificial viscosity closure models is essential for consistently accurate
dynamics forecasting of incompressible fluid flows
Design of a large dynamic range readout unit for the PSD detector of DAMPE
A large dynamic range is required by the Plastic Scintillator Detector (PSD)
of DArk Matter Paricle Explorer (DAMPE), and a double-dynode readout has been
developed. To verify this design, a prototype detector module has been
constructed and tested with cosmic rays and heavy ion beams. The results match
with the estimation and the readout unit could easily cover the required
dynamic range
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