Implicit neural networks based on point convolution and voxel convolution have been successfully used for point cloud surface reconstruction. This paper proposes an adaptive fusion dual-branch convolutional occupancy ...
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Researchers over the past few decades have proposed specialized algorithms for image encryption, in order to solve the slow encryption speed and high computational requirements of traditional encryption algorithms. Th...
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With the advancement of Mobile Edge Computing (MEC), effective solutions for communication scenarios, including the industrial Internet of Things (IoT) and the Internet of Vehicles (IoV), are becoming increasingly fea...
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Face Presentation Attack Detection(fPAD)plays a vital role in securing face recognition systems against various presentation *** supervised learning-based methods demonstrate effectiveness,they are prone to overfittin...
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Face Presentation Attack Detection(fPAD)plays a vital role in securing face recognition systems against various presentation *** supervised learning-based methods demonstrate effectiveness,they are prone to overfitting to known attack types and struggle to generalize to novel attack *** studies have explored formulating fPAD as an anomaly detection problem or one-class classification task,enabling the training of generalized models for unknown attack ***,conventional anomaly detection approaches encounter difficulties in precisely delineating the boundary between bonafide samples and unknown *** address this challenge,we propose a novel framework focusing on unknown attack detection using exclusively bonafide facial data during *** core innovation lies in our pseudo-negative sample synthesis(PNSS)strategy,which facilitates learning of compact decision boundaries between bonafide faces and potential attack ***,PNSS generates synthetic negative samples within low-likelihood regions of the bonafide feature space to represent diverse unknown attack *** overcome the inherent imbalance between positive and synthetic negative samples during iterative training,we implement a dual-loss mechanism combining focal loss for classification optimization with pairwise confusion loss as a *** architecture effectively mitigates model bias towards bonafide samples while maintaining discriminative *** evaluations across three benchmark datasets validate the framework’s superior ***,our PNSS achieves 8%–18% average classification error rate(ACER)reduction compared with state-of-the-art one-class fPAD methods in cross-dataset evaluations on Idiap Replay-Attack and MSU-MFSD datasets.
This paper proposes a high stable conformal technique based on finite difference time domain (FDTD) method for solving electromagnetic problem of curved metallic objects. The objective is to mitigate the staircase err...
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Against the size issue that DC motor controller uses multiple chips to implement, the chiplized effort is made based on FPGA in this paper. RISC-V ISA based HE203 (Hummingbird E203) is as the core, in which redundant ...
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A graph G is locally irregular if no two of its adjacent vertices have the same degree. The authors of [Fioravantes et al. Complexity of finding maximum locally irregular induced subgraph. SWAT, 2022] introduced and p...
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Sleep staging serves as a fundamental assessment for sleep quality measurement and sleep disorder diagnosis. Although current deep learning approaches have successfully integrated multimodal sleep signals, enhancing t...
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Sleep staging serves as a fundamental assessment for sleep quality measurement and sleep disorder diagnosis. Although current deep learning approaches have successfully integrated multimodal sleep signals, enhancing the accuracy of automatic sleep staging, certain challenges remain, as follows: 1) optimizing the utilization of multi-modal information complementarity, 2) effectively extracting both long- and short-range temporal features of sleep information, and 3) addressing the class imbalance problem in sleep data. To address these challenges, this paper proposes a two-stream encode-decoder network, named TSEDSleepNet, which is inspired by the depth sensitive attention and automatic multi-modal fusion (DSA2F) framework. In TSEDSleepNet, a two-stream encoder is used to extract the multiscale features of electrooculogram (EOG) and electroencephalogram (EEG) signals. And a self-attention mechanism is utilized to fuse the multiscale features, generating multi-modal saliency features. Subsequently, the coarser-scale construction module (CSCM) is adopted to extract and construct multi-resolution features from the multiscale features and the salient features. Thereafter, a Transformer module is applied to capture both long- and short-range temporal features from the multi-resolution features. Finally, the long- and short-range temporal features are restored with low-layer details and mapped to the predicted classification results. Additionally, the Lovász loss function is applied to alleviate the class imbalance problem in sleep datasets. Our proposed method was tested on the Sleep-EDF-39 and Sleep-EDF-153 datasets, and it achieved classification accuracies of 88.9% and 85.2% and Macro-F1 scores of 84.8% and 79.7%, respectively, thus outperforming conventional traditional baseline models. These results highlight the efficacy of the proposed method in fusing multi-modal information. This method has potential for application as an adjunct tool for diagnosing sleep disorde
Skin cancer has become a major global health issue, necessitating improved diagnostic methods for better clinical outcomes. This study thoroughly evaluates deep learning methods for classifying skin cancer using dermo...
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The COVID-19 pandemic has had a significant impact on student life and academic performance. Moreover, different demographic groups have experienced the pandemic very differently. An important factor in the student le...
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