The paper“Fixed-point quantum continuous search algorithm with optimal query complexity”[1]presents another interesting application of quantum search algorithms by addressing one of the long-standing challenges in q...
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The paper“Fixed-point quantum continuous search algorithm with optimal query complexity”[1]presents another interesting application of quantum search algorithms by addressing one of the long-standing challenges in quantum computing:how to efficiently perform search over continuous *** Grover’s algorithm has been a cornerstone in discrete quantum search with its well-known quadratic speedup[2],many real-world problems—ranging from high-dimensional optimization to spectral analysis of infinite dimensional operators—require searching over continuous,uncountably infinite solution spaces.
Unsupervised visible-infrared person re-identification (US-VI-ReID) centers on learning a cross-modality retrieval model without labels, reducing the reliance on expensive cross-modality manual annotation. Previous US...
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ISBN:
(数字)9798350353006
ISBN:
(纸本)9798350353013
Unsupervised visible-infrared person re-identification (US-VI-ReID) centers on learning a cross-modality retrieval model without labels, reducing the reliance on expensive cross-modality manual annotation. Previous US-VI-ReID works gravitate toward learning cross-modality information with the deep features extracted from the ultimate layer. Nevertheless, interfered by the multiple discrepancies, solely relying on deep features is insufficient for accurately learning modality-invariant features, resulting in negative optimization. The shallow feature from the shallow layers contains nuanced detail information, which is critical for effective cross-modality learning but is dis- regarded regrettably by the existing methods. To address the above issues, we design a Shallow-Deep Collaborative Learning (SDCL) framework based on the transformer with shallow-deep contrastive learning, incorporating Collaborative Neighbor Learning (CNL) and Collaborative Ranking Association (CRA) module. Specifically, CNL unveils the intrinsic homogeneous and heterogeneous collaboration which are harnessed for neighbor alignment, enhancing the robustness in a dynamic manner. Furthermore, CRA associates the cross-modality labels with the ranking association between shallow and deep features, furnishing valuable supervision for cross-modality learning. Extensive experiments validate the superiority of our method, even outperforming certain supervised counterparts.
Android platform offers a hybrid concurrency model encompassing multi-threading and asynchronous messaging for concurrent programming. The model is powerful but complex, making it difficult for developers to analyze c...
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ISBN:
(数字)9798350330663
ISBN:
(纸本)9798350330670
Android platform offers a hybrid concurrency model encompassing multi-threading and asynchronous messaging for concurrent programming. The model is powerful but complex, making it difficult for developers to analyze concurrent behaviors. Data race, a prevalent concurrency defect in real-world Android applications, often results in abnormal executions of mobile applications, even crashes. Despite quite a few studies on detecting data races, it still suffers from high false positives with static analysis techniques and high false negatives with dynamic analysis techniques. To address this issue, this paper presents a predictive approach, PredRacer, for detecting data races in Android applications. It first captures an execution trace of an Android application, and then reorders the events within the trace based on partial orders. Finally, it checks the feasibility of the generated event sequence, which contains a potential data race. PredRacer increases the search scope and reduces false negatives while mitigating false positives by incorporating the happen-before relations specific to the Android concurrency model. The effectiveness of PredRacer is evaluated using the BenchERoid data set. Experimental results demonstrate that PredRacer achieves high precision, recall, and F1 score, outperforming the state-of-the-art techniques. A collection of 20 open-source Android applications is further utilized to assess the effectiveness of PredRacer, and an evaluation of 300 wild apps is conducted to assess its efficiency and scalability.
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
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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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.
The popular constitutive models used in the field of hot forming of magnesium alloys can be divided into phenomenological models, machine learning models, and internal state variables (ISV) models based on physical me...
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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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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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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.
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