In the increasingly digitized world, the privacy and security of sensitive data shared via IoT devices are paramount. Traditional privacy-preserving methods like k-anonymity and l-diversity are becoming outdated due t...
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Recently, some weakly supervised multi-object tracking (MOT) methods learn identity embedding features with pseudo identity labels rather than the high-cost manual ones. However, these pseudo identity labels may conta...
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Multiplex networks are collections of networks with identical nodes but distinct layers of edges. They are genuine representations of a large variety of real systems whose elements interact in multiple fashions or fla...
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Multiplex networks are collections of networks with identical nodes but distinct layers of edges. They are genuine representations of a large variety of real systems whose elements interact in multiple fashions or flavors. However, multiplex networks are not always simple to observe in the real world; often, only partial information on the layer structure of the networks is available, whereas the remaining information is in the form of aggregated, single-layer networks. Recent works have proposed solutions to the problem of reconstructing the hidden multiplexity of single-layer networks using tools proper for network science. Here, we develop a machine-learning framework that takes advantage of graph embeddings, i.e., representations of networks in geometric space. We validate the framework in systematic experiments aimed at the reconstruction of synthetic and real-world multiplex networks, providing evidence that our proposed framework not only accomplishes its intended task, but often outperforms existing reconstruction techniques.
Channel pruning has been long studied to compress convolutional neural networks (CNNs), which significantly reduces the overall computation. Prior works implement channel pruning in an unexplainable manner, which tend...
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Deep reinforcement learning can seamlessly transfer agile locomotion and navigation skills from the simulator to real world. However, bridging the sim-to-real gap with domain randomization or adversarial methods often...
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Modern data centers suffer from a growing carbon footprint due to insufficient support for environmental sustainability. While hardware accelerators and renewable energy have been utilized to enhance sustainability, a...
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Resource consumption is increasing globally. Global resource consumption has surged by over 65%, contributing to 70% of greenhouse gases (GHGs), raising concerns for future generations. Circular economy offers a strat...
Current quantum generative adversarial networks (QGANs) still struggle with practical-sized data. First, many QGANs use principal component analysis (PCA) for dimension reduction, which, as our studies reveal, can dim...
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Myocardial infarction or MI is a medical condition that occurs when the supply of oxygen-rich blood to the heart muscle is interrupted or stops suddenly. As a result, the part of the heart muscle that does not get eno...
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ISBN:
(数字)9798350365351
ISBN:
(纸本)9798350365368
Myocardial infarction or MI is a medical condition that occurs when the supply of oxygen-rich blood to the heart muscle is interrupted or stops suddenly. As a result, the part of the heart muscle that does not get enough blood supply begins to suffer damage or death. Research related to MI using ECG signals has started since the 1900s. As technology and computing advances, analysis of ECG signals with more sophisticated methods, such as digital image processing and signal processing techniques, has been developed to improve accuracy and reliability in detecting myocardial infarction. But it does not provide detailed information about the interaction between embedding features. This research provides a solution to the above problem by developing a CNN-based MI model that will be optimized by Hyperparameter-tuning. This technique is proven to produce a solid and accurate MI model. However, it is important to run a comprehensive evaluation and validation of these models, including testing on existing data sets and peers. The methods used in the development of MI models with CNNs are as follows. First, this study examines the performance of ECG data using CNN algorithms, then the results will be compared with the performance of ECG data using Hyperparameter-tuning. The result of this research is the MI model with CNN Non-Tuning method has an accuracy of 77% and for CNN Fine-Tuning method has an accuracy of 80%.
Recent Siamese trackers have taken advantage of transformers to achieve impressive advancements. However, existing transformer trackers ignore considering the positional and structural information between tokens, and ...
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