CNNs and Transformers have significantly advanced the domain of medical image segmentation. The integration of their strengths facilitates rich feature extraction but also introduces the challenge of mixed multi-scale...
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With the increasing use of cloud computing,high energy consumption has become one of the major challenges in cloud data *** Machine(VM)consolidation has been proven to be an efficient way to optimize energy consumptio...
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With the increasing use of cloud computing,high energy consumption has become one of the major challenges in cloud data *** Machine(VM)consolidation has been proven to be an efficient way to optimize energy consumption in data centers,and many research works have proposed to optimize VM ***,the performance of different algorithms is related with the characteristics of the workload and system status;some algorithms are suitable for Central Processing Unit(CPU)-intensive workload and some for web application ***,an adaptive VM consolidation framework is necessary to fully explore the potential of these *** is an open-source dynamic VM consolidation framework,which is well integrated into ***,it cannot conduct dynamic algorithm scheduling,and VM consolidation algorithms in Neat are few and basic,which results in low performance for energy saving and Service-Level Agreement(SLA)*** this paper,an Intelligent Neat framework(I-Neat)is proposed,which adds an intelligent scheduler using reinforcement learning and a framework manager to improve the usability of the *** scheduler can select appropriate algorithms for the local manager from an algorithm library with many load detection *** algorithm library is designed based on a template,and in addition to the algorithms of Neat,I-Neat adds six new algorithms to the algorithm ***,the framework manager helps users add self-defined algorithms to I-Neat without modifying the source *** experimental results indicate that the intelligent scheduler and these novel algorithms can effectively reduce energy consumption with SLA assurance.
With the rise of encrypted traffic,traditional network analysis methods have become less effective,leading to a shift towards deep learning-based *** these,multimodal learning-based classification methods have gained ...
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With the rise of encrypted traffic,traditional network analysis methods have become less effective,leading to a shift towards deep learning-based *** these,multimodal learning-based classification methods have gained attention due to their ability to leverage diverse feature sets from encrypted traffic,improving classification ***,existing research predominantly relies on late fusion techniques,which hinder the full utilization of deep features within the *** address this limitation,we propose a novel multimodal encrypted traffic classification model that synchronizes modality fusion with multiscale feature ***,our approach performs real-time fusion of modalities at each stage of feature extraction,enhancing feature representation at each level and preserving inter-level correlations for more effective *** continuous fusion strategy improves the model’s ability to detect subtle variations in encrypted traffic,while boosting its robustness and adaptability to evolving network *** results on two real-world encrypted traffic datasets demonstrate that our method achieves a classification accuracy of 98.23% and 97.63%,outperforming existing multimodal learning-based methods.
In recent years, with the rapid development of the Internet of Vehicles (IoVs) and the widespread application of integrated sensing and communication (ISAC) in the IoVs, the integrated sensing and computation offloadi...
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Trajectory privacy protection schemes based on suppression strategies rarely take geospatial constraints into account,which is made more likely for an attacker to determine the user’s true sensitive location and *** ...
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Trajectory privacy protection schemes based on suppression strategies rarely take geospatial constraints into account,which is made more likely for an attacker to determine the user’s true sensitive location and *** solve this problem,this paper presents a privacy budget allocation method based on privacy security level(PSL).Firstly,in a custom map,the idea of P-series is contributed to allocate a given total privacy budget reasonably to the initially sensitive ***,the size of privacy security level for sensitive locations is dynamically adjusted by comparing it with the customized initial level threshold parameterµ.Finally,the privacy budget of the initial sensitive location is allocated to its neighbors based on the relationship between distance and degree between *** comparing the PSL algorithm with the traditional allocation methods,the results show that it is more flexible to allocate a privacy budget without compromising location privacy under the same preset conditions.
The article investigates the issue of fixed-time control with adaptive output feedback for a twin-roll inclined casting system (TRICS) with disturbance. First, by using the mean value theorem, the nonaffine functions ...
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The rapid rise of cyberattacks and the gradual failure of traditional defense systems and approaches led to using artificial intelligence(AI)techniques(such as machine learning(ML)and deep learning(DL))to build more e...
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The rapid rise of cyberattacks and the gradual failure of traditional defense systems and approaches led to using artificial intelligence(AI)techniques(such as machine learning(ML)and deep learning(DL))to build more efficient and reliable intrusion detection systems(IDSs).However,the advent of larger IDS datasets has negatively impacted the performance and computational complexity of AI-based *** researchers used data preprocessing techniques such as feature selection and normalization to overcome such *** most of these researchers reported the success of these preprocessing techniques on a shallow level,very few studies have been performed on their effects on a wider ***,the performance of an IDS model is subject to not only the utilized preprocessing techniques but also the dataset and the ML/DL algorithm used,which most of the existing studies give little emphasis ***,this study provides an in-depth analysis of feature selection and normalization effects on IDS models built using three IDS datasets:NSL-KDD,UNSW-NB15,and CSE–CIC–IDS2018,and various AI algorithms.A wrapper-based approach,which tends to give superior performance,and min-max normalization methods were used for feature selection and normalization,*** IDS models were implemented using the full and feature-selected copies of the datasets with and without *** models were evaluated using popular evaluation metrics in IDS modeling,intra-and inter-model comparisons were performed between models and with state-of-the-art *** forest(RF)models performed better on NSL-KDD and UNSW-NB15 datasets with accuracies of 99.86%and 96.01%,respectively,whereas artificial neural network(ANN)achieved the best accuracy of 95.43%on the CSE–CIC–IDS2018 *** RF models also achieved an excellent performance compared to recent *** results show that normalization and feature selection positively affect IDS ***,while feature sel
A Bayesian network security situational awareness technique based on Bayesian networks is proposed to address the current problem of the inability to analyze and predict the security situation of established networks ...
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Facial beauty analysis is an important topic in human *** may be used as a guidance for face beautification applications such as cosmetic *** neural networks(DNNs)have recently been adopted for facial beauty analysis ...
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Facial beauty analysis is an important topic in human *** may be used as a guidance for face beautification applications such as cosmetic *** neural networks(DNNs)have recently been adopted for facial beauty analysis and have achieved remarkable ***,most existing DNN-based models regard facial beauty analysis as a normal classification *** ignore important prior knowledge in traditional machine learning models which illustrate the significant contribution of the geometric features in facial beauty *** be specific,landmarks of the whole face and facial organs are introduced to extract geometric features to make the *** by this,we introduce a novel dual-branch network for facial beauty analysis:one branch takes the Swin Transformer as the backbone to model the full face and global patterns,and another branch focuses on the masked facial organs with the residual network to model the local patterns of certain facial ***,the designed multi-scale feature fusion module can further facilitate our network to learn complementary semantic information between the two *** model optimisation,we propose a hybrid loss function,where especially geometric regulation is introduced by regressing the facial landmarks and it can force the extracted features to convey facial geometric *** performed on the SCUT-FBP5500 dataset and the SCUT-FBP dataset demonstrate that our model outperforms the state-of-the-art convolutional neural networks models,which proves the effectiveness of the proposed geometric regularisation and dual-branch structure with the hybrid *** the best of our knowledge,this is the first study to introduce a Vision Transformer into the facial beauty analysis task.
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