Composite Tor traffic features extraction method of webpage in actual network flow based on SDN by Hongping YAN, Qiang ZHOU, Shihao WANG, Wang YAO, Liukun HE, Liangmin WANG

“…Website fingerprinting (WF) methods for Tor webpage traffic are often based on the separated Tor traffic or even the separated Tor webpage traffic.However, distinguishing Tor traffic from the original traffic of the actual network and Tor webpage traffic from the Tor traffic costs amount of computation, which is more difficult than the WF attack itself.According to the current architecture of the Internet and the characteristics of network traffic converging to regional central nodes, the bi-directional statistical feature (BSF) was proposed for distinguishing Tor traffic through the intra-domain global perspective provided by the SDN structure of the central node and the node information disclosed by the Tor network.Furthermore, a hidden feature extraction method for Web traffic based on lifted structure fingerprinting (LSF) was proposed, and a composited Tor-webpage-identification traffic feature (CTTF) was proposed based on BSF and LSF deep features.For solving the problem of traffic training data scarcity, a traffic data augmentation method based on translation was proposed, which made the augmented traffic data as consistent as the Tor traffic data captured in the real working environment.The experimental results show that the identification rate based on CTTF can be improved by about 4% compared with using only the original data features.When there is less training data, the classification accuracy is improved more obvious after using the traffic data augmentation method, and the false positive rate can be effectively reduced.…”
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DIGITALISATION OF TEACHING AND LEARNING METHODS IN STATISTICS by DANIELA-EMANUELA DĂNĂCICĂ

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Transfer learning and the early estimation of single-photon source quality using machine learning methods by David Jacob Kedziora, Anna Musiał, Wojciech Rudno-Rudziński, Bogdan Gabrys

“…Validation metrics quickly reveal that even a linear regressor can outperform standard fitting when it is tested on the same contexts it was trained on, but the success of transfer learning is less assured, even though statistical analysis, made possible by data augmentation, suggests its superiority as an early estimator. …”
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Quantum-Inspired Statistical Frameworks: Enhancing Traditional Methods with Quantum Principles by Theodoros Kyriazos, Mary Poga

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A Hybrid Algorithm with a Data Augmentation Method to Enhance the Performance of the Zero-Inflated Bernoulli Model by Chih-Jen Su, I-Fei Chen, Tzong-Ru Tsai, Yuhlong Lio

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GES: A New Building Damage Data Augmentation and Detection Method Based on Extremely Imbalanced Data and Unique Spatial Distribution of Satellite Images by Xiaopeng Sha, Zhoupeng Guo, Xinqi Sang, Shuyu Wang, Yuliang Zhao

“…To address the issues of extreme class distribution imbalance and spatial distribution uniqueness, this article proposes a new data augmentation method called the geospatial enhancement sampling (GES) algorithm. …”
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Data Imputation Based on Retrieval-Augmented Generation by Xiaojun Shi, Jiacheng Wang, Gregorius Justin Chung, Derick Julian, Lianpeng Qiao

“…However, these repositories often suffer from issues such as incomplete, inconsistent, and low-quality data, which hinder data-driven insights. Existing methods for data imputation, including statistical techniques and machine learning approaches, often rely heavily on large amounts of labeled data and domain-specific knowledge, making them labor-intensive and limited in handling semantic heterogeneity across data formats. …”
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Statistical resolution of ambiguous HLA typing data. by Jennifer Listgarten, Zabrina Brumme, Carl Kadie, Gao Xiaojiang, Bruce Walker, Mary Carrington, Philip Goulder, David Heckerman

“…However, high-resolution HLA typing is frequently unavailable due to its high cost or the inability to re-type historical data. In this paper, we introduce and evaluate a method for statistical, in silico refinement of ambiguous and/or low-resolution HLA data. …”
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Air Data Sensor Fault Detection with an Augmented Floating Limiter by Fabio Balzano, Mario L. Fravolini, Marcello R. Napolitano, Stéphane d’Urso, Michele Crispoltoni, Giuseppe del Core

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Impact of SAR Image Quantization Method on Target Recognition With Neural Networks by Kangwei Li, Di Wang, Daoxiang An

“…Experimental results indicate that models trained with adaptive quantization can learn more general features; linear quantization exhibits poor generalization when not enhanced, but this can be improved through data augmentation. Furthermore, pretraining and data augmentation techniques significantly enhance the classification performance of models under different quantization strategies, providing scientific evidence for optimizing SAR imaging system design and constructing reasonable datasets.…”
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Data Augmentation for Improving Convergence Speed in Federated Sequential Recommendation System by Donghoon Lee, Hyunsouk Cho

“…We aim to systematically evaluate six data augmentation methods and their effectiveness in mitigating statistical heterogeneity for efficient federated sequential recommendation. …”
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An augmented GSNMF model for complete deconvolution of bulk RNA-seq data by Shaoyu Li, Su Xu, Xue Wang, Nilüfer Ertekin-Taner, Duan Chen

“…Using these strategies, we developed a new pipeline of pseudo-bulk tissue data augmented, geometric structure guided NMF model (GSNMF$ + $). …”
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Noise-agnostic quantum error mitigation with data augmented neural models by Manwen Liao, Yan Zhu, Giulio Chiribella, Yuxiang Yang

“…Abstract Quantum error mitigation, a data processing technique for recovering the statistics of target processes from their noisy version, is a crucial task for near-term quantum technologies. …”
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Functional cognitive performance augments cognitive screening data in older adults by Timothy S. Marks, Gordon Muir Giles, Dorothy Farrar Edwards

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Electrohysterogram Data Augmentation Using Generative Adversarial Network for Pregnancy Outcome Prediction by Muhammad Omar Cheema, Zia Mohy-Ud-Din, Azhar Imran, Fawad Salam Khan, Mahmood Basil A. Al-Rawi, Mohammed A. El-Meligy, Jahan Zeb Gul

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An Efficient Radio Map Construction Method Using Region-Aware Adaptive Ensemble Regression for Indoor Localization by Farid Yuli Martin Adiyatma, Panarat Cherntanomwong

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Analysis of user interaction with virtual objects in augmented reality applications by M. V. Alpatova

“…A large amount of collected experimental data is statistically analyzed to make sure that the previously proposed method of optimal augmented reality object placement really simplifies the user experience and reduces the time needed for object placement. …”
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Augmented robustness in home demand prediction: Integrating statistical loss function with enhanced cross-validation in machine learning hyperparameter optimisation by Banafshe Parizad, Ali Jamali, Hamid Khayyam

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Data Augmentation Approaches for Estimating Curtain Wall Construction Duration in High-Rise Buildings by Sang-Jun Park, Jin-Bin Im, Hye-Soon Yoon, Ju-Hyung Kim

“…The results showed that SMOTE and SMOTE–Tomek best represented the original dataset based on box plot analysis showcasing data distribution. Moreover, according to statistical performance criteria, it was found that the oversampling techniques improved the prediction performance, where Pearson correlation for linear, polynomial, and RBF increased by 0.611%, 4.232%, and 0.594%, respectively, for the best-performing sampling method. …”
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GANs for data augmentation with stacked CNN models and XAI for interpretable maize yield prediction by Ishaan Seshukumar Pothapragada, Sujatha R

“…For augmenting data to address the issue of data-scarcity, generative adversarial networks (GANs) with a 200-dimension latent space were used to synthetically generate 20,000 samples, which greatly boosted the dataset. …”
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