Electronics

Research

12 pages, 1186 KiB

Open AccessArticle

Optimal Transport-Embedded Neural Network for Fairness Transfer Problem

by Muchao Xiang, Zaixun Ling, Qine Liu and Yaoxuan Zhang

Electronics 2023, 12(21), 4481; https://doi.org/10.3390/electronics12214481 - 31 Oct 2023

Viewed by 748

Research on neuromorphic computing has gained popularity in recent years. In particular, regularized embedded neural systems have been applied in several significant real-world situations, such as recommendation systems and transfer learning. This paper deals with the fairness transfer learning problem, which has been [...] Read more.

Research on neuromorphic computing has gained popularity in recent years. In particular, regularized embedded neural systems have been applied in several significant real-world situations, such as recommendation systems and transfer learning. This paper deals with the fairness transfer learning problem, which has been insufficiently explored. In fairness transfer settings, the source domain has limit-tagged training samples, which may lead to performance degradation in the target domain. To solve such problems, a linear data-augmentation-based optimal transport-embedded neural network is proposed in this paper. It can augment the source samples to make the distribution of the source domain balanced and can align the source and target distributions simultaneously. Moreover, the distribution of the augmented data by mixup is limited to a certain bound that can avoid the abnormal samples generated. The effectiveness of the proposed method has been demonstrated in several transfer learning tests, including regression and classification. In 1-shot and 3-shot classification tasks on the Office dataset, our method’s accuracy is 4.8 and 3.9% better, respectively, than the second-best model. Additionally, our model’s performance is about 2–3 percentage points superior to the second-best model in the OfficeHome dataset. It is simple yet effective, making it perfect for low-power edge AI applications. Full article

(This article belongs to the Special Issue Recent Advances and Related Technologies in Neuromorphic Computing)

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13 pages, 3549 KiB

Open AccessArticle

CPL-Net: A Malware Detection Network Based on Parallel CNN and LSTM Feature Fusion

by Jun Lu, Xiaokai Ren, Jiaxin Zhang and Ting Wang

Electronics 2023, 12(19), 4025; https://doi.org/10.3390/electronics12194025 - 25 Sep 2023

Cited by 1 | Viewed by 1163

Abstract

Malware is a significant threat to the field of cyber security. There is a wide variety of malware, which can be programmed to threaten computer security by exploiting various networks, operating systems, software and physical security vulnerabilities. So, detecting malware has become a [...] Read more.

Malware is a significant threat to the field of cyber security. There is a wide variety of malware, which can be programmed to threaten computer security by exploiting various networks, operating systems, software and physical security vulnerabilities. So, detecting malware has become a significant part of maintaining network security. In this paper, data enhancement techniques are used in the data preprocessing stage, then a novel detection mode—CPL-Net employing malware texture image—is proposed. The model consists of a feature extraction component, a feature fusion component and a classification component, the core of which is based on the parallel fusion of spatio-temporal features by Convolutional Neural Networks (CNN) and Long Short-Term Memory networks (LSTM). Through experiments, it has been proven that CPL-Net can achieve an accuracy of 98.7% and an F1 score of 98.6% for malware. The model uses a novel feature fusion approach and achieves a comprehensive and precise malware detection. Full article

(This article belongs to the Special Issue Recent Advances and Related Technologies in Neuromorphic Computing)

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18 pages, 8037 KiB

Open AccessArticle

Optimal Reconstruction of Single-Pixel Images through Feature Feedback Mechanism and Attention

by Zijun Gao, Jingwen Su, Junjie Zhang, Zhankui Song, Bo Li and Jue Wang

Electronics 2023, 12(18), 3838; https://doi.org/10.3390/electronics12183838 - 11 Sep 2023

Viewed by 776

Abstract

The single-pixel imaging technique can reconstruct high-quality images using only a bucket detector with no spatial resolution, and the image quality is degraded in order to meet the demands of real-time applications. According to some studies of algorithm performance, the network model performs [...] Read more.

The single-pixel imaging technique can reconstruct high-quality images using only a bucket detector with no spatial resolution, and the image quality is degraded in order to meet the demands of real-time applications. According to some studies of algorithm performance, the network model performs differently in simulated and real-world experiments. We propose an end-to-end neural network capable of reconstructing 2D images from experimentally obtained 1D signals optimally. In order to improve the image quality of real-time single-pixel imaging, we built a feedback module in the hidden layer of the recurrent neural network to implement feature feedback. The feedback module fuses high-level features of undersampled images with low-level features through dense jump connections and multi-scale balanced attention modules to gradually optimize the feature extraction process and reconstruct high-quality images. In addition, we introduce a learning strategy that combines mean loss with frequency domain loss to improve the network’s ability to reconstruct complex undersampled images. In this paper, the factors that lead to the degradation of single-pixel imaging are analyzed, and a network degradation model suitable for physical imaging systems is designed. The experiment results indicate that the reconstructed images utilizing the proposed method have better quality metrics and visual effects than the excellent methods in the field of single-pixel imaging. Full article

(This article belongs to the Special Issue Recent Advances and Related Technologies in Neuromorphic Computing)

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