198 research outputs found
TSFool: Crafting Highly-imperceptible Adversarial Time Series through Multi-objective Black-box Attack to Fool RNN Classifiers
Neural network (NN) classifiers are vulnerable to adversarial attacks.
Although the existing gradient-based attacks achieve state-of-the-art
performance in feed-forward NNs and image recognition tasks, they do not
perform as well on time series classification with recurrent neural network
(RNN) models. This is because the cyclical structure of RNN prevents direct
model differentiation and the visual sensitivity of time series data to
perturbations challenges the traditional local optimization objective of the
adversarial attack. In this paper, a black-box method called TSFool is proposed
to efficiently craft highly-imperceptible adversarial time series for RNN
classifiers. We propose a novel global optimization objective named Camouflage
Coefficient to consider the imperceptibility of adversarial samples from the
perspective of class distribution, and accordingly refine the adversarial
attack as a multi-objective optimization problem to enhance the perturbation
quality. To get rid of the dependence on gradient information, we also propose
a new idea that introduces a representation model for RNN to capture deeply
embedded vulnerable samples having otherness between their features and latent
manifold, based on which the optimization solution can be heuristically
approximated. Experiments on 10 UCR datasets are conducted to confirm that
TSFool averagely outperforms existing methods with a 46.3% higher attack
success rate, 87.4% smaller perturbation and 25.6% better Camouflage
Coefficient at a similar time cost.Comment: 9 pages, 7 figure
Throughput Maximization Leveraging Just-Enough SNR Margin and Channel Spacing Optimization
Flexible optical network is a promising technology to accommodate
high-capacity demands in next-generation networks. To ensure uninterrupted
communication, existing lightpath provisioning schemes are mainly done with the
assumption of worst-case resource under-provisioning and fixed channel spacing,
which preserves an excessive signal-to-noise ratio (SNR) margin. However, under
a resource over-provisioning scenario, the excessive SNR margin restricts the
transmission bit-rate or transmission reach, leading to physical layer resource
waste and stranded transmission capacity. To tackle this challenging problem,
we leverage an iterative feedback tuning algorithm to provide a just-enough SNR
margin, so as to maximize the network throughput. Specifically, the proposed
algorithm is implemented in three steps. First, starting from the high SNR
margin setup, we establish an integer linear programming model as well as a
heuristic algorithm to maximize the network throughput by solving the problem
of routing, modulation format, forward error correction, baud-rate selection,
and spectrum assignment. Second, we optimize the channel spacing of the
lightpaths obtained from the previous step, thereby increasing the available
physical layer resources. Finally, we iteratively reduce the SNR margin of each
lightpath until the network throughput cannot be increased. Through numerical
simulations, we confirm the throughput improvement in different networks and
with different baud-rates. In particular, we find that our algorithm enables
over 20\% relative gain when network resource is over-provisioned, compared to
the traditional method preserving an excessive SNR margin.Comment: submitted to IEEE JLT, Jul. 17th, 2021. 14 pages, 8 figure
MCNS: Mining Causal Natural Structures Inside Time Series via A Novel Internal Causality Scheme
Causal inference permits us to discover covert relationships of various
variables in time series. However, in most existing works, the variables
mentioned above are the dimensions. The causality between dimensions could be
cursory, which hinders the comprehension of the internal relationship and the
benefit of the causal graph to the neural networks (NNs). In this paper, we
find that causality exists not only outside but also inside the time series
because it reflects a succession of events in the real world. It inspires us to
seek the relationship between internal subsequences. However, the challenges
are the hardship of discovering causality from subsequences and utilizing the
causal natural structures to improve NNs. To address these challenges, we
propose a novel framework called Mining Causal Natural Structure (MCNS), which
is automatic and domain-agnostic and helps to find the causal natural
structures inside time series via the internal causality scheme. We evaluate
the MCNS framework and impregnation NN with MCNS on time series classification
tasks. Experimental results illustrate that our impregnation, by refining
attention, shape selection classification, and pruning datasets, drives NN,
even the data itself preferable accuracy and interpretability. Besides, MCNS
provides an in-depth, solid summary of the time series and datasets.Comment: 9 pages, 6 figure
Sensing as a Service in 6G Perceptive Mobile Networks: Architecture, Advances, and the Road Ahead
Sensing-as-a-service is anticipated to be the core feature of 6G perceptive
mobile networks (PMN), where high-precision real-time sensing will become an
inherent capability rather than being an auxiliary function as before. With the
proliferation of wireless connected devices, resource allocation in terms of
the users' specific quality-of-service (QoS) requirements plays a pivotal role
to enhance the interference management ability and resource utilization
efficiency. In this article, we comprehensively introduce the concept of
sensing service in PMN, including the types of tasks, the
distinctions/advantages compared to conventional networks, and the definitions
of sensing QoS. Subsequently, we provide a unified RA framework in
sensing-centric PMN and elaborate on the unique challenges. Furthermore, we
present a typical case study named "communication-assisted sensing" and
evaluate the performance trade-off between sensing and communication procedure.
Finally, we shed light on several open problems and opportunities deserving
further investigation in the future
Sensing as a Service in 6G Perceptive Networks: A Unified Framework for ISAC Resource Allocation
In the upcoming next-generation (5G-Advanced and 6G) wireless networks,
sensing as a service will play a more important role than ever before.
Recently, the concept of perceptive network is proposed as a paradigm shift
that provides sensing and communication (S&C) services simultaneously. This
type of technology is typically referred to as Integrated Sensing and
Communications (ISAC). In this paper, we propose the concept of sensing quality
of service (QoS) in terms of diverse applications. Specifically, the
probability of detection, the Cramer-Rao bound (CRB) for parameter estimation
and the posterior CRB for moving target indication are employed to measure the
sensing QoS for detection, localization, and tracking, respectively. Then, we
establish a unified framework for ISAC resource allocation, where the fairness
and the comprehensiveness optimization criteria are considered for the
aforementioned sensing services. The proposed schemes can flexibly allocate the
limited power and bandwidth resources according to both S&C QoSs. Finally, we
study the performance trade-off between S&C services in different resource
allocation schemes by numerical simulations
ITportrait: Image-Text Coupled 3D Portrait Domain Adaptation
Domain adaptation of 3D portraits has gained more and more attention.
However, the transfer mechanism of existing methods is mainly based on vision
or language, which ignores the potential of vision-language combined guidance.
In this paper, we propose a vision-language coupled 3D portraits domain
adaptation framework, namely Image and Text portrait (ITportrait). ITportrait
relies on a two-stage alternating training strategy. In the first stage, we
employ a 3D Artistic Paired Transfer (APT) method for image-guided style
transfer. APT constructs paired photo-realistic portraits to obtain accurate
artistic poses, which helps ITportrait to achieve high-quality 3D style
transfer. In the second stage, we propose a 3D Image-Text Embedding (ITE)
approach in the CLIP space. ITE uses a threshold function to adaptively control
the optimization direction of image or text in the CLIP space. Comprehensive
quantitative and qualitative results show that our ITportrait achieves
state-of-the-art (SOTA) results and benefits downstream tasks. All source codes
and pre-trained models will be released to the public
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