In addressing the multi-path problem of cloud-edge data transmission, this paper introduces a multi-path orchestration method based on deep reinforcement learning (DRL) within the context of software-defined networks ...
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High blood pressure (HBP) is at the root of many cardiovascular diseases, causing millions of deaths every year worldwide. Continuous monitoring of blood pressure (BP) is necessary, not only to prevent cardiovascular ...
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ISBN:
(纸本)9798350312249
High blood pressure (HBP) is at the root of many cardiovascular diseases, causing millions of deaths every year worldwide. Continuous monitoring of blood pressure (BP) is necessary, not only to prevent cardiovascular disease, but also to help those already affected. While many methods of blood pressure measurement exist at present, they present two major inconveniences, these methods do not allow continuous measurement and they are frequently very invasive. In this paper, we present an alternative method to measure systolic and diastolic blood (SBP and DBP) pressure values with photoplethysmographic (PPG) signals using a deep learning architecture. PPG signals come from MIMIC II, a noisy and unprocessed database. Using a ResNet neural network coupled with LSTM layers, we succeeded in predicting SBP and DBP values, with an RMSE of 8.96 mmHg.
With the increasing number of devices and the advent of 5G and 6G networks, ensuring reliable power and data connectivity remains a significant challenge, particularly in rural or remote areas. Simultaneous Wireless I...
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Multiple systems have explored how to use programmable switch ASICs to improve the performance of distributedsystems. However, they focus on accelerating read operations and perform poorly under write-intensive workl...
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ISBN:
(纸本)9798350350128
Multiple systems have explored how to use programmable switch ASICs to improve the performance of distributedsystems. However, they focus on accelerating read operations and perform poorly under write-intensive workloads. In this paper, we present Gecko, a system that accelerates write operations in distributed key-value store systems using switch ASICs. The core idea of Gecko is to offload the client-side chasing mechanism, a technique deployed by production storage networks, to the programmable switch to simultaneously reduce the perceived and actual write-tail latency in distributed key-value stores. The perceived latency is the interval between the user sending a write request and the user receiving the write success, and there may be replicas that have not yet completed the write, but the actual latency requires all replicas to be successfully written. Gecko not only reduces the perceived write tail latency by deploying the chasing mechanism, but the actual write tail latency is also reduced by utilizing the capabilities of programmable switches. Specifically, Gecko's in-network chasing design caches a write request at the switch data plane and reports success to the client when only m out of n (usually set to 2 and 3 in production networks, respectively) replicas have been successfully written to the server, and retries the cached write request if the remaining n - m replicas are not successfully written. In addition to caching the write request, Gecko also introduces novel designs to fully implement the chasing controller and a corresponding timer controller in the switch data plane, minimizing the interaction overhead between the switch control and data plane. Extensive experiments on a testbed of Barefoot Tofino switch and commodity servers show that Gecko not only substantially reduces the write tail latency by more than 2.16x caused by transient glitches at servers, but also maintains the same level of reliability as the classic three-replica write opera
The proliferation of distributednetworks and the increasing demand for remote collaboration and maintenance have highlighted the need for secure and controlled remote access solutions. This paper presents a novel app...
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The quick development of Internet of Things (IoT) has brought about concerns regarding the security of interconnected networks and devices. This requires the utilization of effective Intrusion Detection System (IDS) t...
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After a natural disaster, it is very important to be able to make a building damage assessment quickly and accurately. Considering real-world scenarios, one of the most important issues related to the deep learning ba...
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ISBN:
(纸本)9798350368130
After a natural disaster, it is very important to be able to make a building damage assessment quickly and accurately. Considering real-world scenarios, one of the most important issues related to the deep learning based computer vision models used in this field is their generalization ability. Ideally, we would like to have a global model which is capable of making damage assessment successfully in all disasters in the future. However, this is a difficult task as images of one disaster varies greatly from images of another due to reasons like different geographical region structure, structure of houses, position of clouds, etc. On the other hand, many damage assessment models are evaluated on an unrealistic in-domain (ID) test set, which contains data from the same disasters in the training and test sets. In this study, we focus on the generalization problem and compare the model performance in an ID test setting with that in an out-of-domain (OOD) test setting, which is more realistic since the test data is formed from disasters not in the training set. We show that the performance of the model is comparatively lower for the OOD test setting and there is a large generalization gap in this case. We conclude that the models should be evaluated by using the OOD test setting. Then, we propose to generate post-disaster images and masks by using generative adversarial network (GAN) models and use the generated data to fine-tune the damage assessment model. It is seen that the generalization ability of the damage assessment model is increased by using this approach.
In the field of electricity, in response to the problem of graph neural networks having both significant performance and poor robustness, this may lead to poor recommendation performance or affect social evaluation, p...
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distributed Denial of Service (DDoS) attacks are malicious attacks that aim to disrupt the normal flow of traffic to the targeted server or network by manipulating the server's infrastructure with overflowing inte...
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distributed Denial of Service (DDoS) attacks are malicious attacks that aim to disrupt the normal flow of traffic to the targeted server or network by manipulating the server's infrastructure with overflowing internet traffic. This study aims to investigate several artificial intelligence (AI) models and utilise them in the DDoS detection system. The paper examines how AI is being used to detect DDoS attacks in real-time to find the most accurate methods to improve network security. The machine learning models identified and discussed in this research include random forest, decision tree (DT), convolutional neural network (CNN), NGBoosT classifier, and stochastic gradient descent (SGD). The research findings demonstrate the effectiveness of these models in detecting DDoS attacks. The study highlights the potential for future enhancement of these technologies to enhance the security and privacy of data servers and networks in real-time. Using the qualitative research method and comparing several AI models, research results reveal that the random forest model offers the best detection accuracy (99.9974%). This finding holds significant implications for the enhancement of future DDoS detection systems.
distributed denial-of-service (DDoS) attacks disrupt communication between nodes in distributed Internet of Things (IoT) networks. Numerous protocols have been proposed to detect and prevent these attacks, but most fo...
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