85 research outputs found
Fast and Accurate Neural Word Segmentation for Chinese
Neural models with minimal feature engineering have achieved competitive
performance against traditional methods for the task of Chinese word
segmentation. However, both training and working procedures of the current
neural models are computationally inefficient. This paper presents a greedy
neural word segmenter with balanced word and character embedding inputs to
alleviate the existing drawbacks. Our segmenter is truly end-to-end, capable of
performing segmentation much faster and even more accurate than
state-of-the-art neural models on Chinese benchmark datasets.Comment: To appear in ACL201
AsdKB: A Chinese Knowledge Base for the Early Screening and Diagnosis of Autism Spectrum Disorder
To easily obtain the knowledge about autism spectrum disorder and help its
early screening and diagnosis, we create AsdKB, a Chinese knowledge base on
autism spectrum disorder. The knowledge base is built on top of various
sources, including 1) the disease knowledge from SNOMED CT and ICD-10 clinical
descriptions on mental and behavioural disorders, 2) the diagnostic knowledge
from DSM-5 and different screening tools recommended by social organizations
and medical institutes, and 3) the expert knowledge on professional physicians
and hospitals from the Web. AsdKB contains both ontological and factual
knowledge, and is accessible as Linked Data at https://w3id.org/asdkb/. The
potential applications of AsdKB are question answering, auxiliary diagnosis,
and expert recommendation, and we illustrate them with a prototype which can be
accessed at http://asdkb.org.cn/.Comment: 17 pages, Accepted by the Resource Track of ISWC 202
Identifying risk factors affecting exercise behavior among overweight or obese individuals in China
BackgroundThe disease burden caused by obesity has increased significantly in China. Less than 30% of those who are obese meet the weekly physical activity standards recommended by the WHO. Risk factors that influence exercise behavior in people with obesity remain unclear.MethodsBased on the survey from the Chinese General Social Survey program (CGSS) in 2017, 3,331 subjects were identified and enrolled in the univariate and multiple probit regression models. We aimed to identify the association between SRH and the exercise behavior of obese people and further explore the influencing factors of active physical activity in this group of people.ResultsThe proportion of active physical activity in obese people was 25%. Groups with better SRH, higher education and income were more likely to participate in sports. Obese people who lived in rural areas, were unmarried or divorced, or fell within the age range of 35–40 had a significantly lower percentage of engagement in active physical activity.ConclusionsThe proportion of people with obesity who meet the WHO recommendation for physical activity in China is not ideal. Health promotion programs for those who are obese need to be further strengthened and targeted, especially for rural areas, low-income families, and middle-aged obese people
Black-Box Dissector: Towards Erasing-based Hard-Label Model Stealing Attack
Previous studies have verified that the functionality of black-box models can
be stolen with full probability outputs. However, under the more practical
hard-label setting, we observe that existing methods suffer from catastrophic
performance degradation. We argue this is due to the lack of rich information
in the probability prediction and the overfitting caused by hard labels. To
this end, we propose a novel hard-label model stealing method termed
\emph{black-box dissector}, which consists of two erasing-based modules. One is
a CAM-driven erasing strategy that is designed to increase the information
capacity hidden in hard labels from the victim model. The other is a
random-erasing-based self-knowledge distillation module that utilizes soft
labels from the substitute model to mitigate overfitting. Extensive experiments
on four widely-used datasets consistently demonstrate that our method
outperforms state-of-the-art methods, with an improvement of at most .
We also validate the effectiveness and practical potential of our method on
real-world APIs and defense methods. Furthermore, our method promotes other
downstream tasks, \emph{i.e.}, transfer adversarial attacks
Scale Invariant Fully Convolutional Network: Detecting Hands Efficiently
Existing hand detection methods usually follow the pipeline of multiple
stages with high computation cost, i.e., feature extraction, region proposal,
bounding box regression, and additional layers for rotated region detection. In
this paper, we propose a new Scale Invariant Fully Convolutional Network
(SIFCN) trained in an end-to-end fashion to detect hands efficiently.
Specifically, we merge the feature maps from high to low layers in an iterative
way, which handles different scales of hands better with less time overhead
comparing to concatenating them simply. Moreover, we develop the Complementary
Weighted Fusion (CWF) block to make full use of the distinctive features among
multiple layers to achieve scale invariance. To deal with rotated hand
detection, we present the rotation map to get rid of complex rotation and
derotation layers. Besides, we design the multi-scale loss scheme to accelerate
the training process significantly by adding supervision to the intermediate
layers of the network. Compared with the state-of-the-art methods, our
algorithm shows comparable accuracy and runs a 4.23 times faster speed on the
VIVA dataset and achieves better average precision on Oxford hand detection
dataset at a speed of 62.5 fps.Comment: Accepted to AAAI201
Towards Language-guided Visual Recognition via Dynamic Convolutions
In this paper, we are committed to establishing an unified and end-to-end
multi-modal network via exploring the language-guided visual recognition. To
approach this target, we first propose a novel multi-modal convolution module
called Language-dependent Convolution (LaConv). Its convolution kernels are
dynamically generated based on natural language information, which can help
extract differentiated visual features for different multi-modal examples.
Based on the LaConv module, we further build the first fully language-driven
convolution network, termed as LaConvNet, which can unify the visual
recognition and multi-modal reasoning in one forward structure. To validate
LaConv and LaConvNet, we conduct extensive experiments on four benchmark
datasets of two vision-and-language tasks, i.e., visual question answering
(VQA) and referring expression comprehension (REC). The experimental results
not only shows the performance gains of LaConv compared to the existing
multi-modal modules, but also witness the merits of LaConvNet as an unified
network, including compact network, high generalization ability and excellent
performance, e.g., +4.7% on RefCOCO+
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