In 2020, the pandemic has triggered a rapid paradigm shift and necessitated a prompt switch in teaching methods worldwide. This significant change has affected the educators to rethink and assess the notion of teachin...
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Large-scale CT image studies often suffer from a lack of homogeneity regarding radiomic characteristics due to the images acquired with scanners from different vendors or with different reconstruction algorithms. We p...
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ISBN:
(数字)9781665468190
ISBN:
(纸本)9781665468206
Large-scale CT image studies often suffer from a lack of homogeneity regarding radiomic characteristics due to the images acquired with scanners from different vendors or with different reconstruction algorithms. We propose a deep learning-based framework called UDA-CT to tackle the homogeneity issue by leveraging both paired and unpaired images. Using UDA-CT, the CT images can be standardized both from different acquisition protocols of the same scanner and CT images acquired using a similar protocol but scanners from different vendors. UDA-CT incorporates recent advances in deep learning including domain adaptation and adversarial augmentation. It includes a unique design for model training batch which integrates nonstandard images and their adversarial variations to enhance model generalizability. The experimental results show that UDA-CT significantly improves the performance of the cross-scanner image standardization by utilizing both paired and unpaired data.
We present a new sparse Gaussian process regression model whose covariance function is parameterized by the locations of a progressively growing set of pseudo-inputs generated by an online deterministic annealing opti...
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ISBN:
(数字)9781665467612
ISBN:
(纸本)9781665467629
We present a new sparse Gaussian process regression model whose covariance function is parameterized by the locations of a progressively growing set of pseudo-inputs generated by an online deterministic annealing optimization algorithm. A series of entropy-regularized optimization problems is solved sequentially, introducing a bifurcation phenomenon, according to which, pseudo-inputs are gradually generated. This results in an active learning approach, which, in contrast to most existing works, can modify already selected pseudo-inputs and is trained using a recursive gradient-free stochastic approximation algorithm. Finally, the proposed algorithm is able to incorporate prior knowledge in the form of a probability density, according to which new observations are sampled. Experimental results showcase the efficacy and potential advantages of the proposed methodology.
Modern Resource-Constrained (RC) Internet of Things (IoT) devices are subject to several types of attacks, including hardware-level attacks. Most of the existing state-of-the-art solutions are invasive, require expens...
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ISBN:
(纸本)9781450396141
Modern Resource-Constrained (RC) Internet of Things (IoT) devices are subject to several types of attacks, including hardware-level attacks. Most of the existing state-of-the-art solutions are invasive, require expensive design time interventions, or need dataset generation from non-trusted RC-IoT devices or both. We argue that the health of modern RC-IoT devices requires a final line of defense against.possible hardware attacks that go undetected during the IC design and test process. Hence, in this paper, we propose a defense methodology against.non-zero-day and zero-day attacks, leveraging machine learning techniques trained on the dataset obtained without design time intervention and using ‘only’ trusted IoT devices. In the process, a complete eco-system is developed where data is generated through a trusted group of devices, and machine learning is done on these trusted datasets. Next, this trusted trained model is deployed in regular IoT systems that contain untrusted devices, where the attack on untrusted devices can be detected in real-time. Our results indicate that for non-zero-day attacks, the proposed technique can concurrently detect DoS and power depletion attacks with an accuracy of about 80%. Similarly, zero-day attack experiments are able to detect the attack without fail as well.
Breast cancer stands as the most frequently diagnosed life-threatening cancer among women worldwide. Understanding patients' drug experiences is essential to improving treatment strategies and outcomes. In this re...
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With the increasing growth of information through smart devices, increasing the quality level of human life requires various computational paradigms presentation including the Internet of Things, fog, and cloud. Betwe...
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Recently, automated biometric identification system (ABIS) has wide applications involving automatic identification and data capture (AIDC), which includes automatic security checking, verifying personal identity to p...
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Vital sign (breathing and heartbeat) monitoring is essential for patient care and sleep disease prevention. Most current solutions are based on wearable sensors or cameras;however, the former could affect sleep qualit...
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The emergence of cloud computing technology has motivated a very high number of users in different organizations to access its services in running and delivering their various operations and services. However, this su...
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ISBN:
(数字)9798350358155
ISBN:
(纸本)9798350358162
The emergence of cloud computing technology has motivated a very high number of users in different organizations to access its services in running and delivering their various operations and services. However, this surge of cloud users has led to the problem of uneven load sharing among the cloud computing infrastructures. As a result, federated cloud infrastructure was initiated to provide more cloud computing resources to accommodate more cloud users’ requests and also to provide equal requests mapping with the available cloud resources. Nevertheless, the issue of unequal allocation of requests within the datacenter(s) that makes up the federated cloud environment still persists. This research work presents an intra-datacenter load balancing algorithm in a federated cloud environment named; the intra-balancer adapted throttled algorithm to evenly distribute requests in each datacenter that formulate the federated cloud infrastructures. The simulation of the intra-balancer was carried out in CloudAnalyst, the results of the implementation showed that the intra-balancer outperformed the Round-Robin and ESCE with 98.93ms and 2.26ms for the response and processing time respectively. The results assert that the intra-balancer algorithm provides a better load balancing solution in a federated cloud environment.
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