The Internet of Everything(IoE)based cloud computing is one of the most prominent areas in the digital big data *** approach allows efficient infrastructure to store and access big real-time data and smart IoE service...
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The Internet of Everything(IoE)based cloud computing is one of the most prominent areas in the digital big data *** approach allows efficient infrastructure to store and access big real-time data and smart IoE services from the *** IoE-based cloud computing services are located at remote locations without the control of the data *** data owners mostly depend on the untrusted Cloud Service Provider(CSP)and do not know the implemented security *** lack of knowledge about security capabilities and control over data raises several security *** Acid(DNA)computing is a biological concept that can improve the security of IoE big *** IoE big data security scheme consists of the Station-to-Station Key Agreement Protocol(StS KAP)and Feistel cipher *** paper proposed a DNA-based cryptographic scheme and access control model(DNACDS)to solve IoE big data security and access *** experimental results illustrated that DNACDS performs better than other DNA-based security *** theoretical security analysis of the DNACDS shows better resistance capabilities.
Purpose: The rapid spread of COVID-19 has resulted in significant harm and impacted tens of millions of people globally. In order to prevent the transmission of the virus, individuals often wear masks as a protective ...
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Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) have been actively employed for tasks that are challenging for humans, such as scenarios like battlefield and disaster. Swarm unmanned systems, compr...
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Acute Bilirubin Encephalopathy(ABE)is a significant threat to neonates and it leads to disability and high mortality *** and treating ABE promptly is important to prevent further complications and long-term *** studie...
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Acute Bilirubin Encephalopathy(ABE)is a significant threat to neonates and it leads to disability and high mortality *** and treating ABE promptly is important to prevent further complications and long-term *** studies have explored ABE ***,they often face limitations in classification due to reliance on a single modality of Magnetic Resonance Imaging(MRI).To tackle this problem,the authors propose a Tri-M2MT model for precise ABE detection by using tri-modality MRI *** scans include T1-weighted imaging(T1WI),T2-weighted imaging(T2WI),and apparent diffusion coefficient maps to get indepth ***,the tri-modality MRI scans are collected and preprocessesed by using an Advanced Gaussian Filter for noise reduction and Z-score normalisation for data *** Advanced Capsule Network was utilised to extract relevant features by using Snake Optimization Algorithm to select optimal features based on feature correlation with the aim of minimising complexity and enhancing detection ***,a multi-transformer approach was used for feature fusion and identify feature correlations ***,accurate ABE diagnosis is achieved through the utilisation of a SoftMax *** performance of the proposed Tri-M2MT model is evaluated across various metrics,including accuracy,specificity,sensitivity,F1-score,and ROC curve analysis,and the proposed methodology provides better performance compared to existing methodologies.
This paper demonstrates the novel approach of sub-micron-thick InGaAs broadband photodetectors(PDs)designed for high-resolution imaging from the visible to short-wavelength infrared(SWIR)*** approaches encounter chall...
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This paper demonstrates the novel approach of sub-micron-thick InGaAs broadband photodetectors(PDs)designed for high-resolution imaging from the visible to short-wavelength infrared(SWIR)*** approaches encounter challenges such as low resolution and crosstalk issues caused by a thick absorption layer(AL).Therefore,we propose a guided-mode resonance(GMR)structure to enhance the quantum efficiency(QE)of the InGaAs PDs in the SWIR region with only sub-micron-thick *** TiOx/Au-based GMR structure compensates for the reduced AL thickness,achieving a remarkably high QE(>70%)from 400 to 1700 nm with only a 0.98μm AL InGaAs PD(defined as 1μm AL PD).This represents a reduction in thickness by at least 2.5 times compared to previous results while maintaining a high ***,the rapid transit time is highly expected to result in decreased electrical *** effectiveness of the GMR structure is evident in its ability to sustain QE even with a reduced AL thickness,simultaneously enhancing the transit *** breakthrough offers a viable solution for high-resolution and low-noise broadband image sensors.
Predictability is an essential challenge for autonomous vehicles(AVs)’*** neural networks have been widely deployed in the AV’s perception ***,it is still an open question on how to guarantee the perception predicta...
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Predictability is an essential challenge for autonomous vehicles(AVs)’*** neural networks have been widely deployed in the AV’s perception ***,it is still an open question on how to guarantee the perception predictability for AV because there are millions of deep neural networks(DNNs)model combinations and system configurations when deploying DNNs in *** paper proposes configurable predictability testbed(CPT),a configurable testbed for quantifying the predictability in AV’s perception *** provides flexible configurations of the perception pipeline on data,DNN models,fusion policy,scheduling policies,and predictability *** top of CPT,the researchers can profile and optimize the predictability issue caused by different application and system *** has been open-sourced at:https://***/Torreskai0722/CPT.
Semi-supervised learning techniques utilize both labeled and unlabeled images to enhance classification performance in scenarios where labeled images are limited. However, challenges such as integrating unlabeled imag...
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Semi-supervised learning techniques utilize both labeled and unlabeled images to enhance classification performance in scenarios where labeled images are limited. However, challenges such as integrating unlabeled images with incorrect pseudo-labels, determining appropriate thresholds for the pseudo-labels, and label prediction fluctuations on low-confidence unlabeled images, hinder the effectiveness of existing methods. This research introduces a novel framework named Interpolation Consistency for Bad Generative Adversarial Networks (IC-BGAN) that utilizes a new loss function. The proposed model combines bad adversarial training, fusion techniques, and regularization to address the limitations of semi-supervised learning. IC-BGAN creates three types of image augmentations and label consistency regularization in interpolation of bad fake images, real and bad fake images, and unlabeled images. It demonstrates linear interpolation behavior, reducing fluctuations in predictions, improving stability, and facilitating the identification of decision boundaries in low-density areas. The regularization techniques boost the discriminative capability of the classifier and discriminator, and send a better signal to the bad generator. This improves the generalization and the generation of diverse inter-class fake images as support vectors with information near the true decision boundary, which helps to correct the pseudo-labeling of unlabeled images. The proposed approach achieves notable improvements in error rate from 2.87 to 1.47 on the Modified National Institute of Standards and Technology (MNIST) dataset, 3.59 to 3.13 on the Street View House Numbers (SVHN) dataset, and 12.13 to 9.59 on the Canadian Institute for Advanced Research, 10 classes (CIFAR-10) dataset using 1000 labeled training images. Additionally, it reduces the error rate from 22.11 to 18.40 on the CINIC-10 dataset when using 700 labeled images per class. The experiments demonstrate the IC-BGAN framework outp
Gradient compression is a promising approach to alleviating the communication bottleneck in data parallel deep neural network (DNN) training by significantly reducing the data volume of gradients for synchronization. ...
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Gradient compression is a promising approach to alleviating the communication bottleneck in data parallel deep neural network (DNN) training by significantly reducing the data volume of gradients for synchronization. While gradient compression is being actively adopted by the industry (e.g., Facebook and AWS), our study reveals that there are two critical but often overlooked challenges: 1) inefficient coordination between compression and communication during gradient synchronization incurs substantial overheads, and 2) developing, optimizing, and integrating gradient compression algorithms into DNN systems imposes heavy burdens on DNN practitioners, and ad-hoc compression implementations often yield surprisingly poor system performance. In this paper, we propose a compression-aware gradient synchronization architecture, CaSync, which relies on flexible composition of basic computing and communication primitives. It is general and compatible with any gradient compression algorithms and gradient synchronization strategies and enables high-performance computation-communication pipelining. We further introduce a gradient compression toolkit, CompLL, to enable efficient development and automated integration of on-GPU compression algorithms into DNN systems with little programming burden. Lastly, we build a compression-aware DNN training framework HiPress with CaSync and CompLL. HiPress is open-sourced and runs on mainstream DNN systems such as MXNet, TensorFlow, and PyTorch. Evaluation via a 16-node cluster with 128 NVIDIA V100 GPUs and a 100 Gbps network shows that HiPress improves the training speed over current compression-enabled systems (e.g., BytePS-onebit, Ring-DGC and PyTorch-PowerSGD) by 9.8%-69.5% across six popular DNN models. IEEE
We consider a power system whose electric demand pertaining to freshwater production is high(high freshwater electric demand),as in the Middle East,and investigate the tradeoff of storing freshwater in tanks versus st...
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We consider a power system whose electric demand pertaining to freshwater production is high(high freshwater electric demand),as in the Middle East,and investigate the tradeoff of storing freshwater in tanks versus storing electricity in batteries at the day-ahead operation *** storing freshwater and storing electricity increase the actual electric demand at valley hours and decrease it at peak hours,which is generally beneficial in term of cost and ***,to what extent?We analyze this question considering three power systems with different generation-mix configurations,i.e.,a thermal-dominated mix,a renewable-dominated one,and a fully renewable *** generation-mix configurations are inspired by how power systems may evolve in different countries in the Middle *** production uncertainty is compactly modeled using chance *** draw conclusions on how both storage facilities(freshwater and electricity)complement each other to render an optimal operation of the power system.
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