160 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
Text Classification Based on Knowledge Graphs and Improved Attention Mechanism
To resolve the semantic ambiguity in texts, we propose a model, which
innovatively combines a knowledge graph with an improved attention mechanism.
An existing knowledge base is utilized to enrich the text with relevant
contextual concepts. The model operates at both character and word levels to
deepen its understanding by integrating the concepts. We first adopt
information gain to select import words. Then an encoder-decoder framework is
used to encode the text along with the related concepts. The local attention
mechanism adjusts the weight of each concept, reducing the influence of
irrelevant or noisy concepts during classification. We improve the calculation
formula for attention scores in the local self-attention mechanism, ensuring
that words with different frequencies of occurrence in the text receive higher
attention scores. Finally, the model employs a Bi-directional Gated Recurrent
Unit (Bi-GRU), which is effective in feature extraction from texts for improved
classification accuracy. Its performance is demonstrated on datasets such as
AGNews, Ohsumed, and TagMyNews, achieving accuracy of 75.1%, 58.7%, and 68.5%
respectively, showing its effectiveness in classifying tasks
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
Directed network comparison using motifs
Analyzing and characterizing the differences between networks is a
fundamental and challenging problem in network science. Previously, most
network comparison methods that rely on topological properties have been
restricted to measuring differences between two undirected networks. However,
many networks, such as biological networks, social networks, and transportation
networks, exhibit inherent directionality and higher-order attributes that
should not be ignored when comparing networks. Therefore, we propose a
motif-based directed network comparison method that captures local, global, and
higher-order differences between two directed networks. Specifically, we first
construct a motif distribution vector for each node, which captures the
information of a node's involvement in different directed motifs. Then, the
dissimilarity between two directed networks is defined on the basis of a matrix
which is composed of the motif distribution vector of every node and
Jensen-Shannon divergence. The performance of our method is evaluated via the
comparison of six real directed networks with their null models as well as
their perturbed networks based on edge perturbation. Our method is superior to
the state-of-the-art baselines and is robust with different parameter settings
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
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