325 research outputs found
When Attackers Meet AI: Learning-empowered Attacks in Cooperative Spectrum Sensing
Defense strategies have been well studied to combat Byzantine attacks that
aim to disrupt cooperative spectrum sensing by sending falsified versions of
spectrum sensing data to a fusion center. However, existing studies usually
assume network or attackers as passive entities, e.g., assuming the prior
knowledge of attacks is known or fixed. In practice, attackers can actively
adopt arbitrary behaviors and avoid pre-assumed patterns or assumptions used by
defense strategies. In this paper, we revisit this security vulnerability as an
adversarial machine learning problem and propose a novel learning-empowered
attack framework named Learning-Evaluation-Beating (LEB) to mislead the fusion
center. Based on the black-box nature of the fusion center in cooperative
spectrum sensing, our new perspective is to make the adversarial use of machine
learning to construct a surrogate model of the fusion center's decision model.
We propose a generic algorithm to create malicious sensing data using this
surrogate model. Our real-world experiments show that the LEB attack is
effective to beat a wide range of existing defense strategies with an up to 82%
of success ratio. Given the gap between the proposed LEB attack and existing
defenses, we introduce a non-invasive method named as influence-limiting
defense, which can coexist with existing defenses to defend against LEB attack
or other similar attacks. We show that this defense is highly effective and
reduces the overall disruption ratio of LEB attack by up to 80%
Recommended from our members
Trends in HIV prevalence and risk behaviours among men who have sex with men from 2013 to 2017 in Nanjing, China: a consecutive cross-sectional survey.
OBJECTIVE:To examine the trends of HIV prevalence, risk behaviours and HIV testing among men who have sex with men (MSM) in Nanjing. DESIGN:Five consecutive cross-sectional surveys. SETTING:Nanjing, China. PRIMARY AND SECONDARY OUTCOME MEASURES:HIV and syphilis prevalence, HIV testing rate and factors associated with HIV infection; demographic characteristics and behaviours. RESULTS:649, 669, 577, 633, 503 MSM were recruited from 2013 to 2017. HIV prevalence was 9.9%, 12.3%, 12.5%, 9.8% and 10.1%, respectively. Syphilis prevalence decreased with a range from 10.6% to 5.6%. Risk behaviours like unprotected anal intercourse (UAI) and unprotected virginal sex in the past 6 months decreased, but multiple sex partners and ever used rush popper rose significantly. MSM tested for HIV in the previous year remained stable from 57.0% to 64.1% (P=0.633). Multivariate analysis showed that tested for HIV in the past year was protective factor against HIV infection. MSM who had UAI in the past 6 months, sex role as receptive and dual, diagnosed with sexually transmitted diseases (STDs) in the past year and currently syphilis infected were risk factors for HIV infection. CONCLUSIONS:We observed stable high HIV prevalence, a steady HIV testing rate, decreasing syphilis prevalence and UAI among MSM in Nanjing. However, rush popper use rose dramatically. The HIV preventive strategies for MSM including condom promotion, HIV testing expansion and reduction of rush popper use, STDs screening and standardised treatment should be strengthened
Development of helium turbine loss model based on knowledge transfer with Neural Network and its application on aerodynamic design
Helium turbines are widely used in the Closed Brayton Cycle for power
generation and aerospace applications. The primary concerns of designing highly
loaded helium turbines include choosing between conventional and
contra-rotating designs and the guidelines for selecting design parameters. A
loss model serving as an evaluation means is the key to addressing this issue.
Due to the property disparities between helium and air, turbines utilizing
either as working fluid experience distinct loss mechanisms. Consequently,
directly applying gas turbine experience to the design of helium turbines leads
to inherent inaccuracies. A helium turbine loss model is developed by combining
knowledge transfer and the Neural Network method to accurately predict
performance at design and off-design points. By utilizing the loss model,
design parameter selection guidelines for helium turbines are obtained. A
comparative analysis is conducted of conventional and contra-rotating helium
turbine designs. Results show that the prediction errors of the loss model are
below 0.5% at over 90% of test samples, surpassing the accuracy achieved by the
gas turbine loss model. Design parameter selection guidelines for helium
turbines differ significantly from those based on gas turbine experience. The
contra-rotating helium turbine design exhibits advantages in size, weight, and
aerodynamic performance
Multi-Clue Reasoning with Memory Augmentation for Knowledge-based Visual Question Answering
Visual Question Answering (VQA) has emerged as one of the most challenging
tasks in artificial intelligence due to its multi-modal nature. However, most
existing VQA methods are incapable of handling Knowledge-based Visual Question
Answering (KB-VQA), which requires external knowledge beyond visible contents
to answer questions about a given image. To address this issue, we propose a
novel framework that endows the model with capabilities of answering more
general questions, and achieves a better exploitation of external knowledge
through generating Multiple Clues for Reasoning with Memory Neural Networks
(MCR-MemNN). Specifically, a well-defined detector is adopted to predict
image-question related relation phrases, each of which delivers two
complementary clues to retrieve the supporting facts from external knowledge
base (KB), which are further encoded into a continuous embedding space using a
content-addressable memory. Afterwards, mutual interactions between
visual-semantic representation and the supporting facts stored in memory are
captured to distill the most relevant information in three modalities (i.e.,
image, question, and KB). Finally, the optimal answer is predicted by choosing
the supporting fact with the highest score. We conduct extensive experiments on
two widely-used benchmarks. The experimental results well justify the
effectiveness of MCR-MemNN, as well as its superiority over other KB-VQA
methods
Cross-Modal Reasoning with Event Correlation for Video Question Answering
Video Question Answering (VideoQA) is a very attractive and challenging
research direction aiming to understand complex semantics of heterogeneous data
from two domains, i.e., the spatio-temporal video content and the word sequence
in question. Although various attention mechanisms have been utilized to manage
contextualized representations by modeling intra- and inter-modal relationships
of the two modalities, one limitation of the predominant VideoQA methods is the
lack of reasoning with event correlation, that is, sensing and analyzing
relationships among abundant and informative events contained in the video. In
this paper, we introduce the dense caption modality as a new auxiliary and
distill event-correlated information from it to infer the correct answer. To
this end, we propose a novel end-to-end trainable model, Event-Correlated Graph
Neural Networks (EC-GNNs), to perform cross-modal reasoning over information
from the three modalities (i.e., caption, video, and question). Besides the
exploitation of a brand new modality, we employ cross-modal reasoning modules
for explicitly modeling inter-modal relationships and aggregating relevant
information across different modalities, and we propose a question-guided
self-adaptive multi-modal fusion module to collect the question-oriented and
event-correlated evidence through multi-step reasoning. We evaluate our model
on two widely-used benchmark datasets and conduct an ablation study to justify
the effectiveness of each proposed component
Spatially Nonuniform Oscillations in Ferrimagnets Based on an Atomistic Model
The ferrimagnets, such as GdxFeCo(1-x), can produce ultrafast magnetic
switching and oscillation due to the strong exchange field. The two-sublattices
macrospin model has been widely used to explain the experimental results.
However, it fails in describing the spatial nonuniform magnetic dynamics which
gives rises to many important phenomenons such as the domain walls and
skyrmions. Here we develop the two-dimensional atomistic model and provide a
torque analysis method to study the ferrimagnetic oscillation. Under the
spin-transfer torque, the magnetization oscillates in the exchange mode or the
flipped exchange mode. When the Gd composition is increased, the exchange mode
firstly disappears, and then appears again as the magnetization compensation
point is reached. We show that these results can only be explained by analyzing
the spatial distribution of magnetization and effective fields. In particular,
when the sample is small, a spatial nonuniform oscillation is also observed in
the square film. Our work reveals the importance of spatial magnetic
distributions in understanding the ferrimagnetic dynamics. The method developed
in this paper provides an important tool to gain a deeper understanding of
ferrimagnets and antiferromagnets. The observed ultrafast dynamics can also
stimulate the development of THz oscillators.Comment: 17 pages, 4 figure
RDFC-GAN: RGB-Depth Fusion CycleGAN for Indoor Depth Completion
The raw depth image captured by indoor depth sensors usually has an extensive
range of missing depth values due to inherent limitations such as the inability
to perceive transparent objects and the limited distance range. The incomplete
depth map with missing values burdens many downstream vision tasks, and a
rising number of depth completion methods have been proposed to alleviate this
issue. While most existing methods can generate accurate dense depth maps from
sparse and uniformly sampled depth maps, they are not suitable for
complementing large contiguous regions of missing depth values, which is common
and critical in images captured in indoor environments. To overcome these
challenges, we design a novel two-branch end-to-end fusion network named
RDFC-GAN, which takes a pair of RGB and incomplete depth images as input to
predict a dense and completed depth map. The first branch employs an
encoder-decoder structure, by adhering to the Manhattan world assumption and
utilizing normal maps from RGB-D information as guidance, to regress the local
dense depth values from the raw depth map. In the other branch, we propose an
RGB-depth fusion CycleGAN to transfer the RGB image to the fine-grained
textured depth map. We adopt adaptive fusion modules named W-AdaIN to propagate
the features across the two branches, and we append a confidence fusion head to
fuse the two outputs of the branches for the final depth map. Extensive
experiments on NYU-Depth V2 and SUN RGB-D demonstrate that our proposed method
clearly improves the depth completion performance, especially in a more
realistic setting of indoor environments, with the help of our proposed pseudo
depth maps in training.Comment: Haowen Wang and Zhengping Che are with equal contributions. Under
review. An earlier version has been accepted by CVPR 2022 (arXiv:2203.10856
- …