Cloud computing plays a huge role in IT industry, Education sector, government sector, medical field, defense management, Natural disaster control, weather reporting, road traffic maintenances, e-money transaction etc...
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In this modern world due to Road traffic, many people are unable to reach their destination at the correct time. For example, if a person needed to reach the hospital in critical condition due to road traffic, they ar...
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Due to its open-source nature, Android operating system has been the main target of attackers to exploit. Malware creators always perform different code obfuscations on their apps to hide malicious activities. Feature...
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Due to its open-source nature, Android operating system has been the main target of attackers to exploit. Malware creators always perform different code obfuscations on their apps to hide malicious activities. Features extracted from these obfuscated samples through program analysis contain many useless and disguised features, which leads to many false negatives. To address the issue, in this paper, we demonstrate that obfuscation-resilient malware family analysis can be achieved through contrastive learning. The key insight behind our analysis is that contrastive learning can be used to reduce the difference introduced by obfuscation while amplifying the difference between malware and other types of malware. Based on the proposed analysis, we design a system that can achieve robust and interpretable classification of Android malware. To achieve robust classification, we perform contrastive learning on malware samples to learn an encoder that can automatically extract robust features from malware samples. To achieve interpretable classification, we transform the function call graph of a sample into an image by centrality analysis. Then the corresponding heatmaps can be obtained by visualization techniques. These heatmaps can help users understand why the malware is classified as this family. We implement IFDroid and perform extensive evaluations on two datasets. Experimental results show that IFDroid is superior to state-of-the-art Android malware familial classification systems. Moreover, IFDroid is capable of maintaining a 98.4% F1 on classifying 69,421 obfuscated malware samples. IEEE
Traffic speed prediction is vital for intelligent transportation systems. However, most existing methods focus on costly static sensors. In contrast, utilizing GPS devices from vehicles as mobile sensors offers a cost...
Traffic speed prediction is vital for intelligent transportation systems. However, most existing methods focus on costly static sensors. In contrast, utilizing GPS devices from vehicles as mobile sensors offers a cost-effective means to gather dynamic traffic data. Despite the presence of historical trajectory data, mobile sensor-based traffic prediction remains under-explored. Existing methods often treat trajectories as substitutes for static sensors, missing the full utilization of the spatial-temporal signals within the complete trajectory set. To address this, we propose TrajHGT, a novel trajectory set empowered hypergraph transformer model that captures trafficrelated spatial-temporal features through adaptive attention and fusion mechanisms in both the trajectory hypergraph space and the road graph space. Real dataset experiments demonstrate the superiority of TrajHGT.
Cell association is a significant research issue in future mobile communication systems due to the unacceptably large computational time of traditional *** article proposes a polynomial-time cell association scheme wh...
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Cell association is a significant research issue in future mobile communication systems due to the unacceptably large computational time of traditional *** article proposes a polynomial-time cell association scheme which not only completes the association in polynomial time but also fits for a generic optimization objective *** the one hand,traditional cell association as a non-deterministic polynomial(NP)hard problem with a generic utility function is heuristically transformed into a 2-dimensional assignment optimization and solved by a certain polynomial-time algorithm,which significantly saves computational *** the other hand,the scheme jointly considers utility maximization and load balancing among multiple base stations(BSs)by maintaining an experience pool storing a set of weighting factor values and their corresponding *** an association optimization is required,a suitable weighting factor value is taken from the pool to calculate a long square utility matrix and a certain polynomial-time algorithm will be applied for the *** with several representative schemes,the proposed scheme achieves large system capacity and high fairness within a relatively short computational time.
Removing moire patterns from videos recorded on screens or complex textures is known as video demoireing. It is a challenging task as both structures and textures of an image usually exhibit strong periodic patterns, ...
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ISBN:
(纸本)9798350307184
Removing moire patterns from videos recorded on screens or complex textures is known as video demoireing. It is a challenging task as both structures and textures of an image usually exhibit strong periodic patterns, which thus are easily confused with moire patterns and can be significantly erased in the removal process. By interpreting video demoireing as a multi-frame decomposition problem, we propose a compact invertible dyadic network called CIDNet that progressively decouples latent frames and the moire patterns from an input video sequence. Using a dyadic cross-scale coupling structure with coupling layers tailored for multi-scale processing, CIDNet aims at disentangling the features of image patterns from that of moire patterns at different scales, while retaining all latent image features to facilitate reconstruction. In addition, a compressed form for the networks output is introduced to reduce computational complexity and alleviate overfitting. The experiments show that CIDNet outperforms existing methods and enjoys the advantages in model size and computational efficiency.
The integration of artificial intelligence(AI) and digital twin(DT) technology has revolutionized the industrial Internet of Things(IIoT), enabling advanced automation and intelligent manufacturing [1]. Through sophis...
The integration of artificial intelligence(AI) and digital twin(DT) technology has revolutionized the industrial Internet of Things(IIoT), enabling advanced automation and intelligent manufacturing [1]. Through sophisticated interactions between physical entities and their virtual counterparts,AI-driven DTs facilitate performance monitoring, analysis,simulation, and optimization of physical assets, enabling predictive maintenance and informed decision-making [2].
With the proliferation of data-intensive industrial applications, the collaboration of computing powers among standalone edge servers is vital to provision such services for smart devices. In this paper, we propose an...
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Ongoing researches on multiple view data are showing competitive behavior in the machine learning field. Multi-view clustering has gained widespread acceptance for managing multi-view data and improves clustering effi...
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With the rapid advancement of wireless communication technologies, the scarcity of available spectrum resources has become increasingly pronounced. Dynamic Spectrum Access (DSA) emerges as a promising solution to addr...
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