156 research outputs found
An Extensive Game-Based Resource Allocation for Securing D2D Underlay Communications
Device-to-device (D2D) communication has been increasingly attractive due to its great potential to improve cellular communication performance. While resource allocation optimization for improving the spectrum efficiency is of interest in the D2D-related work, communication security, as a key issue in the system design, has not been well investigated yet. Recently, a few studies have shown that D2D users can actually serve as friendly jammers to help enhance the security of cellular user communication against eavesdropping attacks. However, only a few studies considered the security of D2D communications. In this paper, we consider the secure resource allocation problem, particularly, how to assign resources to cellular and the D2D users to maximize the system security. To solve this problem, we propose an extensive game-based algorithm aiming at strengthening the security of both cellular and the D2D communications via system resource allocation. Finally, the simulation results show that the proposed method is able to efficiently improve the overall system security when compared to existing studies
GPT-NAS: Neural Architecture Search with the Generative Pre-Trained Model
Neural Architecture Search (NAS) has emerged as one of the effective methods
to design the optimal neural network architecture automatically. Although
neural architectures have achieved human-level performances in several tasks,
few of them are obtained from the NAS method. The main reason is the huge
search space of neural architectures, making NAS algorithms inefficient. This
work presents a novel architecture search algorithm, called GPT-NAS, that
optimizes neural architectures by Generative Pre-Trained (GPT) model. In
GPT-NAS, we assume that a generative model pre-trained on a large-scale corpus
could learn the fundamental law of building neural architectures. Therefore,
GPT-NAS leverages the generative pre-trained (GPT) model to propose reasonable
architecture components given the basic one. Such an approach can largely
reduce the search space by introducing prior knowledge in the search process.
Extensive experimental results show that our GPT-NAS method significantly
outperforms seven manually designed neural architectures and thirteen
architectures provided by competing NAS methods. In addition, our ablation
study indicates that the proposed algorithm improves the performance of finely
tuned neural architectures by up to about 12% compared to those without GPT,
further demonstrating its effectiveness in searching neural architectures
CoF-CoT: Enhancing Large Language Models with Coarse-to-Fine Chain-of-Thought Prompting for Multi-domain NLU Tasks
While Chain-of-Thought prompting is popular in reasoning tasks, its
application to Large Language Models (LLMs) in Natural Language Understanding
(NLU) is under-explored. Motivated by multi-step reasoning of LLMs, we propose
Coarse-to-Fine Chain-of-Thought (CoF-CoT) approach that breaks down NLU tasks
into multiple reasoning steps where LLMs can learn to acquire and leverage
essential concepts to solve tasks from different granularities. Moreover, we
propose leveraging semantic-based Abstract Meaning Representation (AMR)
structured knowledge as an intermediate step to capture the nuances and diverse
structures of utterances, and to understand connections between their varying
levels of granularity. Our proposed approach is demonstrated effective in
assisting the LLMs adapt to the multi-grained NLU tasks under both zero-shot
and few-shot multi-domain settings.Comment: Accepted at EMNLP 2023 (Main Conference
Aiming in Harsh Environments: A New Framework for Flexible and Adaptive Resource Management
The harsh environment imposes a unique set of challenges on networking
strategies. In such circumstances, the environmental impact on network
resources and long-time unattended maintenance has not been well investigated
yet. To address these challenges, we propose a flexible and adaptive resource
management framework that incorporates the environment awareness functionality.
In particular, we propose a new network architecture and introduce the new
functionalities against the traditional network components. The novelties of
the proposed architecture include a deep-learning-based environment resource
prediction module and a self-organized service management module. Specifically,
the available network resource under various environmental conditions is
predicted by using the prediction module. Then based on the prediction, an
environment-oriented resource allocation method is developed to optimize the
system utility. To demonstrate the effectiveness and efficiency of the proposed
new functionalities, we examine the method via an experiment in a case study.
Finally, we introduce several promising directions of resource management in
harsh environments that can be extended from this paper.Comment: 8 pages, 4 figures, to appear in IEEE Network Magazine, 202
An Entropy-Awareness Meta-Learning Method for SAR Open-Set ATR
Existing synthetic aperture radar automatic target recognition (SAR ATR)
methods have been effective for the classification of seen target classes.
However, it is more meaningful and challenging to distinguish the unseen target
classes, i.e., open set recognition (OSR) problem, which is an urgent problem
for the practical SAR ATR. The key solution of OSR is to effectively establish
the exclusiveness of feature distribution of known classes. In this letter, we
propose an entropy-awareness meta-learning method that improves the
exclusiveness of feature distribution of known classes which means our method
is effective for not only classifying the seen classes but also encountering
the unseen other classes. Through meta-learning tasks, the proposed method
learns to construct a feature space of the dynamic-assigned known classes. This
feature space is required by the tasks to reject all other classes not
belonging to the known classes. At the same time, the proposed
entropy-awareness loss helps the model to enhance the feature space with
effective and robust discrimination between the known and unknown classes.
Therefore, our method can construct a dynamic feature space with discrimination
between the known and unknown classes to simultaneously classify the
dynamic-assigned known classes and reject the unknown classes. Experiments
conducted on the moving and stationary target acquisition and recognition
(MSTAR) dataset have shown the effectiveness of our method for SAR OSR
Semi-Supervised SAR ATR Framework with Transductive Auxiliary Segmentation
Convolutional neural networks (CNNs) have achieved high performance in
synthetic aperture radar (SAR) automatic target recognition (ATR). However, the
performance of CNNs depends heavily on a large amount of training data. The
insufficiency of labeled training SAR images limits the recognition performance
and even invalidates some ATR methods. Furthermore, under few labeled training
data, many existing CNNs are even ineffective. To address these challenges, we
propose a Semi-supervised SAR ATR Framework with transductive Auxiliary
Segmentation (SFAS). The proposed framework focuses on exploiting the
transductive generalization on available unlabeled samples with an auxiliary
loss serving as a regularizer. Through auxiliary segmentation of unlabeled SAR
samples and information residue loss (IRL) in training, the framework can
employ the proposed training loop process and gradually exploit the information
compilation of recognition and segmentation to construct a helpful inductive
bias and achieve high performance. Experiments conducted on the MSTAR dataset
have shown the effectiveness of our proposed SFAS for few-shot learning. The
recognition performance of 94.18\% can be achieved under 20 training samples in
each class with simultaneous accurate segmentation results. Facing variances of
EOCs, the recognition ratios are higher than 88.00\% when 10 training samples
each class
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