The rapid advancement of technology in recent years has brought about numerous changes in various industries, and the financial sector is no exception. The rise of financial technology (FinTech) has disrupted traditio...
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In this paper, we critically examine the limitations of the techno-solutionist approach to explanations in the context of counterfactual generation, reaffirming interactivity as a core value in the explanation interfa...
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Plant diseases have become a challenging threat in the agricultural *** learning approaches for plant disease detection and classification have been adopted to detect and diagnose these diseases ***,deep learning enta...
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Plant diseases have become a challenging threat in the agricultural *** learning approaches for plant disease detection and classification have been adopted to detect and diagnose these diseases ***,deep learning entails extensive data for training,and it may be challenging to collect plant *** though plant datasets can be collected,they may be uneven in *** a result,the problem of classification model overfitting *** study targets this issue and proposes an auxiliary classifier GAN(small-ACGAN)model based on a small number of datasets to extend the available ***,after comparing various attention mechanisms,this paper chose to add the lightweight Coordinate Attention(CA)to the generator module of Auxiliary Classifier GANs(ACGAN)to improve the image ***,a gradient penalty mechanism was added to the loss function to improve the training stability of the *** show that the proposed method can best improve the recognition accuracy of the classifier with the doubled *** AlexNet,the accuracy was increased by 11.2%.In addition,small-ACGAN outperformed the other three GANs used in the ***,the experimental accuracy,precision,recall,and F1 scores of the five convolutional neural network(CNN)classifiers on the enhanced dataset improved by an average of 3.74%,3.48%,3.74%,and 3.80%compared to the original ***,the accuracy of MobileNetV3 reached 97.9%,which fully demonstrated the feasibility of this *** general experimental results indicate that the method proposed in this paper provides a new dataset expansion method for effectively improving the identification accuracy and can play an essential role in expanding the dataset of the sparse number of plant diseases.
The rapid development and usage of digital technologies in modern intelligent systems and applications bring critical challenges on data security and privacy. It is essential to allow cross-organizational data sharing...
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The rapid development and usage of digital technologies in modern intelligent systems and applications bring critical challenges on data security and privacy. It is essential to allow cross-organizational data sharing to achieve smart service provisioning, while preventing unauthorized access and data leak to ensure end users' efficient and secure collaborations. Federated Learning (FL) offers a promising pathway to enable innovative collaboration across multiple organizations. However, more stringent security policies are needed to ensure authenticity of participating entities, safeguard data during communication, and prevent malicious activities. In this paper, we propose a Decentralized Federated Graph Learning (FGL) with Lightweight Zero Trust Architecture (ZTA) model, named DFGL-LZTA, to provide context-aware security with dynamic defense policy update, while maintaining computational and communication efficiency in resource-constrained environments, for highly distributed and heterogeneous systems in next-generation networking. Specifically, with a re-designed lightweight ZTA, which leverages adaptive privacy preservation and reputation-based aggregation together to tackle multi-level security threats (e.g., data-level, model-level, and identity-level attacks), a Proximal Policy Optimization (PPO) based Deep Reinforcement Learning (DRL) agent is introduced to enable the real-time and adaptive security policy update and optimization based on contextual features. A hierarchical Graph Attention Network (GAT) mechanism is then improved and applied to facilitate the dynamic subgraph learning in local training with a layer-wise architecture, while a so-called sparse global aggregation scheme is developed to balance the communication efficiency and model robustness in a P2P manner. Experiments and evaluations conducted based on two open-source datasets and one synthetic dataset demonstrate the usefulness of our proposed model in terms of training performance, computa
Recent face presentation attack detection (PAD) leverages domain adaptation (DA) and domain generalization (DG) techniques to address performance degradation on unknown domains. However, DA-based PAD methods require a...
Recent face presentation attack detection (PAD) leverages domain adaptation (DA) and domain generalization (DG) techniques to address performance degradation on unknown domains. However, DA-based PAD methods require access to unlabeled target data, while most DG-based PAD solutions rely on a priori, i.e., known domain labels. Moreover, most DA-/DG-based methods are computationally intensive, demanding complex model architectures and/or multi-stage training processes. This paper proposes to model face PAD as a compound DG task from a causal perspective, linking it to model optimization. We excavate the causal factors hidden in the high-level representation via counterfactual intervention. Moreover, we introduce a class-guided MixStyle to enrich feature-level data distribution within classes instead of focusing on domain information. Both class-guided MixStyle and counterfactual intervention components introduce no extra trainable parameters and negligible computational resources. Extensive cross-dataset and analytic experiments demonstrate the effectiveness and efficiency of our method compared to state-of-the-art PADs. The implementation and the trained weights are publicly available 1 .
Pneumonia is swelling of the lungs that is usually caused by an infection. This disease is considered as one of the most common reasons for US children to be hospitalized. According to American Thoracic Society (ATS),...
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Federated learning-based model marketplaces have the potential to securely leverage healthcare data for efficient healthcare transactions. However, the willingness to participate in this marketplace is severely hinder...
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The Imaging science Subsystem(ISS)mounted on the Cassini spacecraft has taken a lot of images,which provides an important source of high-precision astrometry of some planets and ***,some of these images are degraded b...
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The Imaging science Subsystem(ISS)mounted on the Cassini spacecraft has taken a lot of images,which provides an important source of high-precision astrometry of some planets and ***,some of these images are degraded by trailed ***,these degraded images cannot be used for *** this paper,a new method is proposed to detect and compute the centers of these trailed stars *** method is then performed on the astrometry of ISS images with trailed ***,we provided 658 astrometric positions between 2004 and 2017 of several satellites that include Enceladus,Dione,Tethys,Mimas and *** with the JPL ephemeris SAT427,the mean residuals of these measurements are 0.11 km and 0.26 km in *** decl.,*** standard deviations are 1.08 km and 1.37 km,*** results show that the proposed method performs astrometric measurements of Cassini ISS images with trailed stars effectively.
This communication presents the study of a new hybrid system composed of a buried rainwater tank thermally activated through a water-to-water heat exchanger. This low-tech solution, scarcely studied in the literature ...
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Longitudinal studies are essential to understand the evolution of individuals’ psychological behaviors, especially in pandemic scenarios. The work proposes the application of the triadic analysis, derived from the th...
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