456 research outputs found
Fast and Accurate Recognition of Chinese Clinical Named Entities with Residual Dilated Convolutions
Clinical Named Entity Recognition (CNER) aims to identify and classify
clinical terms such as diseases, symptoms, treatments, exams, and body parts in
electronic health records, which is a fundamental and crucial task for clinical
and translation research. In recent years, deep learning methods have achieved
significant success in CNER tasks. However, these methods depend greatly on
Recurrent Neural Networks (RNNs), which maintain a vector of hidden activations
that are propagated through time, thus causing too much time to train models.
In this paper, we propose a Residual Dilated Convolutional Neural Network with
Conditional Random Field (RD-CNN-CRF) to solve it. Specifically, Chinese
characters and dictionary features are first projected into dense vector
representations, then they are fed into the residual dilated convolutional
neural network to capture contextual features. Finally, a conditional random
field is employed to capture dependencies between neighboring tags.
Computational results on the CCKS-2017 Task 2 benchmark dataset show that our
proposed RD-CNN-CRF method competes favorably with state-of-the-art RNN-based
methods both in terms of computational performance and training time.Comment: 8 pages, 3 figures. Accepted as regular paper by 2018 IEEE
International Conference on Bioinformatics and Biomedicine. arXiv admin note:
text overlap with arXiv:1804.0501
FedDef: Defense Against Gradient Leakage in Federated Learning-based Network Intrusion Detection Systems
Deep learning (DL) methods have been widely applied to anomaly-based network
intrusion detection system (NIDS) to detect malicious traffic. To expand the
usage scenarios of DL-based methods, the federated learning (FL) framework
allows multiple users to train a global model on the basis of respecting
individual data privacy. However, it has not yet been systematically evaluated
how robust FL-based NIDSs are against existing privacy attacks under existing
defenses. To address this issue, we propose two privacy evaluation metrics
designed for FL-based NIDSs, including (1) privacy score that evaluates the
similarity between the original and recovered traffic features using
reconstruction attacks, and (2) evasion rate against NIDSs using Generative
Adversarial Network-based adversarial attack with the reconstructed benign
traffic. We conduct experiments to show that existing defenses provide little
protection that the corresponding adversarial traffic can even evade the SOTA
NIDS Kitsune. To defend against such attacks and build a more robust FL-based
NIDS, we further propose FedDef, a novel optimization-based input perturbation
defense strategy with theoretical guarantee. It achieves both high utility by
minimizing the gradient distance and strong privacy protection by maximizing
the input distance. We experimentally evaluate four existing defenses on four
datasets and show that our defense outperforms all the baselines in terms of
privacy protection with up to 7 times higher privacy score, while maintaining
model accuracy loss within 3% under optimal parameter combination.Comment: 14 pages, 9 figures, submitted to TIF
MarS3D: A Plug-and-Play Motion-Aware Model for Semantic Segmentation on Multi-Scan 3D Point Clouds
3D semantic segmentation on multi-scan large-scale point clouds plays an
important role in autonomous systems. Unlike the single-scan-based semantic
segmentation task, this task requires distinguishing the motion states of
points in addition to their semantic categories. However, methods designed for
single-scan-based segmentation tasks perform poorly on the multi-scan task due
to the lacking of an effective way to integrate temporal information. We
propose MarS3D, a plug-and-play motion-aware module for semantic segmentation
on multi-scan 3D point clouds. This module can be flexibly combined with
single-scan models to allow them to have multi-scan perception abilities. The
model encompasses two key designs: the Cross-Frame Feature Embedding module for
enriching representation learning and the Motion-Aware Feature Learning module
for enhancing motion awareness. Extensive experiments show that MarS3D can
improve the performance of the baseline model by a large margin. The code is
available at https://github.com/CVMI-Lab/MarS3D
Reducing Objectification Could Tackle Stigma in the COVID-19 Pandemic: Evidence from China
Stigmatization associated with the coronavirus disease 2019 (COVID-19) is expected to be a complex issue and to extend into the later phases of the pandemic, which impairs social cohesion and relevant individuals\u27 well-being. Identifying contributing factors and learning their roles in the stigmatization process may help tackle the problem. This study quantitatively assessed the severity of stigmatization against three different groups of people: people from major COVID-19 outbreak sites, those who had been quarantined, and healthcare workers; explored the factors associated with stigmatization within the frameworks of self-categorization theory and core social motives; and proposed solutions to resolve stigma. The cross-sectional online survey was carried out between April 21 and May 7, 2020, using a convenience sample, which yielded 1,388 valid responses. Employing data analysis methods like multivariate linear regression and moderation analysis, this study yields some main findings: (1) those from major COVID-19 outbreak sites received the highest level of stigma; (2) factors most closely associated with stigmatization, in descending order, are objectification and epidemic proximity in an autonomic aspect and fear of contracting COVID-19 in a controllable aspect; and (3) superordinate categorization is a buffering moderator in objectification-stigmatization relationship. These findings are important for further understanding COVID-19-related stigma, and they can be utilized to develop strategies to fight against relevant discrimination and bias. Specifically, reinforcing superordinate categorization by cultivating common in-group identity, such as volunteering and donating for containment of the pandemic, could reduce objectification and, thus, alleviate stigma
An investigation on nitrogen uptake and microstructure of equimolar quaternary FeCoNiCr high entropy alloy after active-screen plasma nitriding
Under nitrogen diffusion treatments, N-expanded austenite (γN) can form at the surface of self-passivating Fe-Cr, Ni-Cr, and Co-Cr alloys at low temperatures, which provides beneficial hardening and enhancements in wear resistance without reducing corrosion resistance. Given the wide research interests in multicomponent equimolar alloys, an equimolar quaternary FeCoNiCr high entropy alloy (HEA) was investigated after active-screen plasma nitriding at 430–480 °C in this study. Firstly, the formation of γN-FeCoNiCr case at 430 °C was demonstrated with the bright case appearance after metallographic etching, the lattice expansion under XRD, the FCC electron diffraction patterns and the shear bands under TEM. Secondly, the thick treatment cases at ∼9–16 μm first indicated that N interstitial diffusion was not sluggish in the FeCoNiCr surface. Thirdly, analogous to stainless steels, the onset of dark regions in the etched γN-FeCoNiCr case was owing to the formation of a cellular mixture of CrN + γ-(Fe, Co, Ni) nano-lamellae at elevated treatment temperatures. The residual bright regions in γN-FeCoNiCr at 480 °C showed ∼1–3 nm CrN nanoprecipitates with no substantial Cr segregation. Additionally, a significant nanocrystalline layer was seen at the topmost surface at 480 °C, which is most likely associated with the high substrate Cr content
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