91 research outputs found
Joint Head Selection and Airtime Allocation for Data Dissemination in Mobile Social Networks
Mobile social networks (MSNs) enable people with similar interests to
interact without Internet access. By forming a temporary group, users can
disseminate their data to other interested users in proximity with short-range
communication technologies. However, due to user mobility, airtime available
for users in the same group to disseminate data is limited. In addition, for
practical consideration, a star network topology among users in the group is
expected. For the former, unfair airtime allocation among the users will
undermine their willingness to participate in MSNs. For the latter, a group
head is required to connect other users. These two problems have to be properly
addressed to enable real implementation and adoption of MSNs. To this aim, we
propose a Nash bargaining-based joint head selection and airtime allocation
scheme for data dissemination within the group. Specifically, the bargaining
game of joint head selection and airtime allocation is first formulated. Then,
Nash bargaining solution (NBS) based optimization problems are proposed for a
homogeneous case and a more general heterogeneous case. For both cases, the
existence of solution to the optimization problem is proved, which guarantees
Pareto optimality and proportional fairness. Next, an algorithm, allowing
distributed implementation, for join head selection and airtime allocation is
introduced. Finally, numerical results are presented to evaluate the
performance, validate intuitions and derive insights of the proposed scheme
Allocating Limited Resources to Protect a Massive Number of Targets using a Game Theoretic Model
Resource allocation is the process of optimizing the rare resources. In the
area of security, how to allocate limited resources to protect a massive number
of targets is especially challenging. This paper addresses this resource
allocation issue by constructing a game theoretic model. A defender and an
attacker are players and the interaction is formulated as a trade-off between
protecting targets and consuming resources. The action cost which is a
necessary role of consuming resource, is considered in the proposed model.
Additionally, a bounded rational behavior model (Quantal Response, QR), which
simulates a human attacker of the adversarial nature, is introduced to improve
the proposed model. To validate the proposed model, we compare the different
utility functions and resource allocation strategies. The comparison results
suggest that the proposed resource allocation strategy performs better than
others in the perspective of utility and resource effectiveness.Comment: 14 pages, 12 figures, 41 reference
A Multi-Objective Routing Algorithm Based on Auction Game for Space Information Network
This paper aims to create a resource-saving method for the routing problem in space information network. To this end, a multi-objective routing algorithm was created based on game theory for space information network. Specifically, the auction game was introduced to solve the routing problem using the delay-tolerating network (DTN) protocol. Considering the topological periodicity of low earth orbit (LEO) satellite network, a typical space information network, the dynamic topological structure was divided into relatively static time slots. Then, the routing problem was solved through the auction game in these slots. The proposed algorithm can minimize the number of selfish nodes in the network and avoid network congestion resulted from excessive resource consumption of individual nodes. Finally, the proposed algorithm was compared with other well-known routing models like the epidemic routing model (Epidemic) and the first contact routing model (FC). The results show that the proposed algorithm outperformed the contrastive models in both average delay and network overhead ratio. The research findings shed important new light on the routing of space information network
Exploiting W. Ellison model for seawater communication at gigahertz frequencies based on world ocean atlas data
Electromagnetic (EM) waves used to send signals under seawater are normally restricted to low frequencies (f) because of sudden exponential increases of attenuation (α) at higher f. The mathematics of EM wave propagation in seawater demonstrate dependence on relative permeability (μr), relative permittivity (εr), conductivity (σ), and f of transmission. Estimation of εr and σ based on the W. Ellison interpolation model was performed for averaged real‐time data of temperature (T) and salinity (S) from 1955 to 2012 for all oceans with 41088 latitude/longitude points and 101 depth points up to 5500 m. Estimation of parameters such as real and imaginary parts of εr, εr′, εr″, σ, loss tangent (tan δ), propagation velocity (Vp), phase constant (β), and α contributes to absorption loss (La) for seawater channels carried out by using normal distribution fit in the 3 GHz–40 GHz f range. We also estimated total path loss (LPL) in seawater for given transmission power Pt and antenna (dipole) gain. MATLAB is the simulation tool used for analysis
Ultrafast-and-Ultralight ConvNet-Based Intelligent Monitoring System for Diagnosing Early-Stage Mpox Anytime and Anywhere
Due to the lack of more efficient diagnostic tools for monkeypox, its spread
remains unchecked, presenting a formidable challenge to global health. While
the high efficacy of deep learning models for monkeypox diagnosis has been
demonstrated in related studies, the overlook of inference speed, the parameter
size and diagnosis performance for early-stage monkeypox renders the models
inapplicable in real-world settings. To address these challenges, we proposed
an ultrafast and ultralight network named Fast-MpoxNet. Fast-MpoxNet possesses
only 0.27M parameters and can process input images at 68 frames per second
(FPS) on the CPU. To counteract the diagnostic performance limitation brought
about by the small model capacity, it integrates the attention-based feature
fusion module and the multiple auxiliary losses enhancement strategy for better
detecting subtle image changes and optimizing weights. Using transfer learning
and five-fold cross-validation, Fast-MpoxNet achieves 94.26% Accuracy on the
Mpox dataset. Notably, its recall for early-stage monkeypox achieves 93.65%. By
adopting data augmentation, our model's Accuracy rises to 98.40% and attains a
Practicality Score (A new metric for measuring model practicality in real-time
diagnosis application) of 0.80. We also developed an application system named
Mpox-AISM V2 for both personal computers and mobile phones. Mpox-AISM V2
features ultrafast responses, offline functionality, and easy deployment,
enabling accurate and real-time diagnosis for both the public and individuals
in various real-world settings, especially in populous settings during the
outbreak. Our work could potentially mitigate future monkeypox outbreak and
illuminate a fresh paradigm for developing real-time diagnostic tools in the
healthcare field
Unified Language-Vision Pretraining in LLM with Dynamic Discrete Visual Tokenization
Recently, the remarkable advance of the Large Language Model (LLM) has
inspired researchers to transfer its extraordinary reasoning capability to both
vision and language data. However, the prevailing approaches primarily regard
the visual input as a prompt and focus exclusively on optimizing the text
generation process conditioned upon vision content by a frozen LLM. Such an
inequitable treatment of vision and language heavily constrains the model's
potential. In this paper, we break through this limitation by representing both
vision and language in a unified form. Specifically, we introduce a
well-designed visual tokenizer to translate the non-linguistic image into a
sequence of discrete tokens like a foreign language that LLM can read. The
resulting visual tokens encompass high-level semantics worthy of a word and
also support dynamic sequence length varying from the image. Coped with this
tokenizer, the presented foundation model called LaVIT can handle both image
and text indiscriminately under the same generative learning paradigm. This
unification empowers LaVIT to serve as an impressive generalist interface to
understand and generate multi-modal content simultaneously. Extensive
experiments further showcase that it outperforms the existing models by a large
margin on massive vision-language tasks. Our code and models will be available
at https://github.com/jy0205/LaVIT
Multimodal learning via exploring deep semantic similarity
<p>
Deep learning is skilled at learning representation from raw data, which are embedded in the semantic space. Traditional multimodal networks take advantage of this, and maximize the joint distribution over the representations of different modalities. However, the similarity among the representations are not emphasized, which is an important property for multimodal data. In this paper, we will introduce a novel learning method for multimodal networks, named as Semantic Similarity Learning (SSL), which aims at training the model via enhancing the similarity between the highlevel features of different modalities. Sets of experiments are conducted for evaluating the method on different multimodal networks and multiple tasks. The experimental results demonstrate the effectiveness of SSL in keeping the shared information and improving the discrimination. Particularly, SSL shows its ability in encouraging each modality to learn transferred knowledge from the other one when faced with missing data. © 2016 ACM.</p
GA-StackingMD: Android Malware Detection Method Based on Genetic Algorithm Optimized Stacking
With the rapid development of network and mobile communication, intelligent terminals such as smartphones and tablet computers have changed people’s daily life and work. However, malware such as viruses, Trojans, and extortion applications have introduced threats to personal privacy and social security. Malware of the Android operating system has a great variety and updates rapidly. Android malware detection is faced with the problems of high feature dimension and unsatisfied detection accuracy of single classification algorithms. In this work, an Android malware detection framework GA-StackingMD is presented, which employs Stacking to compose five different base classifiers, and Genetic Algorithm is applied to optimize the hyperparameters of the framework. Experiments show that Stacking could effectively improve malware detection accuracy compared with single classifiers. The presented GA-StackingMD achieves 98.43% and 98.66% accuracies on CIC-AndMal2017 and CICMalDroid2020 data sets, which shows the effectiveness and feasibility of the proposed method
A New Hyperchaotic 4D-FDHNN System with Four Positive Lyapunov Exponents and Its Application in Image Encryption
In this paper, a hyperchaotic four-dimensional fractional discrete Hopfield neural network system (4D-FDHNN) with four positive Lyapunov exponents is proposed. Firstly, the chaotic dynamics’ characteristics of the system are verified by analyzing and comparing the iterative trajectory diagram, phase diagram, attractor diagram, 0-1 test, sample entropy, and Lyapunov exponent. Furthermore, a novel image encryption scheme is designed to use the chaotic system as a pseudo-random number generator. In the scenario, the confusion phase using the fractal idea proposes a fractal-like model scrambling method, effectively enhancing the complexity and security of the confusion. For the advanced diffusion phase, we proposed a kind of Hilbert dynamic random diffusion method, synchronously changing the size and location of the pixel values, which improves the efficiency of the encryption algorithm. Finally, simulation results and security analysis experiments show that the proposed encryption algorithm has good efficiency and high security, and can resist common types of attacks
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