Driver’s mental stress is known as a prime factor in road crashes. The devastation of these crashes often results in losses of humans, vehicles, and infrastructure. Likewise, persistent mental stress could develop me...
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Driver’s mental stress is known as a prime factor in road crashes. The devastation of these crashes often results in losses of humans, vehicles, and infrastructure. Likewise, persistent mental stress could develop mental, cardiovascular, and abdominal disorders. Preceding research in this domain mostly focuses on feature engineering and conventional machine learning (ML) approaches. These approaches recognize different stress levels based on handcrafted features extracted from various modalities including physiological, physical, and contextual data. Acquiring the good quality features from these modalities using feature engineering is often a difficult job. The recent developments in the form of deep learning (DL) algorithms have relieved feature engineering by automatically extracting and learning resilient features. Conventional DL models, however, frequently over-fit due to large number of parameters. Thus, large networks face gradient vanishing issues causing an increase in learning failure and generalization errors. Furthermore, it is often hard to acquire a large dataset for training a deep learning model from scratch. To overcome these problems for driver’s stress recognition domain, this paper proposes fast and computationally efficient deep transfer learning models based on Xception pre-trained neural networks. These models classify the driver’s Low, Medium, and High stress levels through electrocardiogram (ECG), heart rate (HR), galvanic skin response (GSR), electromyogram (EMG), and respiration (RESP) signals. Continuous Wavelet Transform (CWT) acquires the scalograms for ECG, HR, GSR, EMG, and RESP signals separately. Then unimodal Xception models are trained based on these scalograms to classify the three stress levels. The proposed Xception models have achieved 97.2%, 86.4%, 82.7%, 71.9%, and 68.9% average validation accuracies based on ECG, RESP, HR, GSR, and EMG signals, respectively. The fuzzy EDAS (evaluation based on distance from average solutio
With the rapid growth of internet content, multimodal long document data has become increasingly prominent, drawing significant attention from researchers. However, most existing methods primarily focus on scenarios w...
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This paper presents BrailleNet, an enhanced one-stage anchor-based object detection model for Braille character recognition, incorporating foreground attention and semantic learning. BrailleNet is validated using two ...
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In the 1990s, the world witnessed a revolutionary breakthrough in the realm of AI-generated art, where its applications surpassed mere visual effects. An ever-increasing number of AI-generating applications emerged, p...
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Continuous learning faces the challenge of catastrophic forgetting. Our research findings indicate that in unsupervised federated continual learning (UFCL), the limited model capacity and interference among participan...
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ISBN:
(数字)9798350390155
ISBN:
(纸本)9798350390162
Continuous learning faces the challenge of catastrophic forgetting. Our research findings indicate that in unsupervised federated continual learning (UFCL), the limited model capacity and interference among participants are the key factors contributing to this problem. Specifically, the fixed capacity of the model restricts its ability to retain historical knowledge. Besides, the indiscriminate aggregation of weights from multiple participants can cause interference, damaging the model memory. To address these challenges, we propose FedFRR, a federated anti-forgetting representation learning approach. FedFRR fits the participants’ data distribution through a weighted combination of primary network units (PNU) in the model and optimizes model memory by adjusting the structure of PNUs. Additionally, FedFRR addresses interference by truncating the PNU with less weight change, thus reducing the scope of weight aggregation. The experimental results demonstrate that FedFRR achieves state-of-the-art performance, significantly enhancing the model’s anti-forgetting ability.
Safety-critical traffic in Industrial Internet of Things (IIoT) requires real-time communications with high fault tolerance, bounded latency and low jitter. Time-Sensitive software-Defined Network (TSSDN), which combi...
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The sensing light source of the line scan camera cannot be fully exposed in a low light environment due to the extremely small number of photons and high noise,which leads to a reduction in image quality.A multi-scale...
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The sensing light source of the line scan camera cannot be fully exposed in a low light environment due to the extremely small number of photons and high noise,which leads to a reduction in image quality.A multi-scale fusion residual encoder-decoder(FRED)was proposed to solve the *** directly learning the end-to-end mapping between light and dark images,FRED can enhance the image’s brightness with the details and colors of the original image fully restored.A residual block(RB)was added to the network structure to increase feature diversity and speed up network ***,the addition of a dense context feature aggregation module(DCFAM)made up for the deficiency of spatial information in the deep network by aggregating the context’s global multi-scale *** experimental results show that the FRED is superior to most other algorithms in visual effect and quantitative evaluation of peak signal-to-noise ratio(PSNR)and structural similarity index measure(SSIM).For the factor that FRED can restore the brightness of images while representing the edge and color of the image effectively,a satisfactory visual quality is obtained under the enhancement of low-light.
Dirty data are prevalent in time series, such as energy consumption or stock data. Existing data cleaning algorithms present shortcomings in dirty data identification and unsatisfactory cleaning decisions. To handle t...
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Dirty data are prevalent in time series, such as energy consumption or stock data. Existing data cleaning algorithms present shortcomings in dirty data identification and unsatisfactory cleaning decisions. To handle these drawbacks, we leverage inherent recurrent patterns in time series, analogize them as fixed combinations in textual data, and incorporate the concept of perplexity. The cleaning problem is thus transformed to minimize the perplexity of the time series under a given cleaning cost, and we design a four-phase algorithmic framework to tackle this problem. To ensure the framework's feasibility, we also conduct a brief analysis of the impact of dirty data and devise an automatic budget selection strategy. Moreover, to make it more generic, we additionally introduce advanced solutions, including an ameliorative probability calculation method grounded in the homomorphic pattern aggregation and a greedy-based heuristic algorithm for resource savings. Experiments on 12 real-world datasets demonstrate the superiority of our methods.
Using deep learning to determine whether a source code file contains defects has become an important research topic. In the past, many researchers have tended to convert code into Abstract Syntax Tree and use deep neu...
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This paper proposes a detector that focuses on multi-scale detection problems and effectively enhances the detection performance to solve the problem that is hard to detect minor traffic signs. This detector, called Y...
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This paper proposes a detector that focuses on multi-scale detection problems and effectively enhances the detection performance to solve the problem that is hard to detect minor traffic signs. This detector, called YOLOF-F (you only look one-level feature fusion), is a single-stage detector that extracts multi-scale feature information from a single layer of fusion feature. First, we propose FFM (feature fusion module) to fuse different scales. Next, we offer a new encoder CDE (corner dilated encoder) to enhance the angular point information in the feature map, improve position regression accuracy, and maintain a faster detection speed. Finally, YOLOF-F achieved 74.57% and 77.23% of the AP on the GTSDB and CTSD datasets and reached 32 FPS. Extensive experiments validate that YOLOF-F is faster and more effective than most traffic sign detection methods.
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