Method based on contrastive incremental learning for fine-grained malicious traffic classification by Yifeng WANG, Yuanbo GUO, Qingli CHEN, Chen FANG, Renhao LIN, Yongliang ZHOU, Jiali MA

“…In order to protect against continuously emerging unknown threats, a new method based on contrastive incremental learning for fine-grained malicious traffic classification was proposed.The proposed method was based on variational auto-encoder (VAE) and extreme value theory (EVT), and the high accuracy could be achieved in known, few-shot and unknown malicious classes and new classes were also identified without using a large number of old task samples, which met the demand of storage and time cost in incremental learning scenarios.Specifically, the contrastive learning was integrated into the encoder of VAE, and the A-Softmax was used for known and few-shot malicious traffic classification, EVT and the decoder of VAE were used for unknown malicious traffic recognition, all classes could be recognized without a lot of old samples when learning new tasks by using VAE reconstruction and knowledge distillation methods.Experimental results indicate that the proposed method is efficient in known, few-shot and unknown malicious classes, and has greatly reduced the forgetting speed of old knowledge in incremental learning scenarios.…”
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UAV-Based Remote Sensing Monitoring of Maize Growth Using Comprehensive Indices by Tingrui Yang, Jinghua Zhao, Ming Hong, Mingjie Ma, Shijiao Ma, Yingying Yuan

“…Comprehensive growth monitoring indices, CGMICV and CGMICT, were developed using the coefficient of variation method (CV) and the technique for order preference by similarity to an ideal solution (TOPSIS) based on the coefficient of variation method of empowerment (Coefficient of variation-TOPSIS, CT) respectively. …”
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Analytical and numerical investigations of slip flow in a Jeffery-Hamel configuration within a converging microchannel incorporating a step variation in wall temperature and the ef... by Elhoucine Essaghir, Youssef Haddout, Mustapha Darif, Abdelaziz Oubarra, Jawad Lahjomri

“…Numerical validation is performed using a second-order finite difference method, showing a high agreement with a maximum deviation error <0.2 %, confirming the accuracy of both methodologies in efficiently resolving the singularity. …”
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Developing Geometry Based Criterion Function Method for Predicting Porosity in LM6 Castings by A. Sata, N. Maheta, H. Khandelwal, S.K. Gautam

“…It is essential to develop a criterion function that considers the impact of geometric variation on the occurrence of shrinkage porosity. …”
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Hydrogen sulfide removal from biogas using chemical absorption technique in packed column reactors by M.B. Kulkarni, P.M. Ghanegaonkar

“…Hydrogen sulfide removal efficiency was calculated for experimental variants like the use of a dedicated purification column, multiple purification columns, flow variations and pressure variations of raw biogas. …”
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Investigating Safe and Economic Adjustment of Power Balance in Smart Grids Based on Integration of Renewable Energy by Hongyan Zhang

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Improving genomic prediction accuracy for methane emission and feed efficiency in sheep: integrating rumen microbial PCA with host genomic variation using neural network GBLUP (NN-... by Setegn Worku Alemu, Timothy P. Bilton, Patricia L. Johnson, Benjamin J. Perry, Hannah Henry, Ken G. Dodds, John C. McEwan, Suzanne J. Rowe

“…For the second objective, the NN-GBLUP model incorporating PCA-reduced RMC data improved prediction accuracy compared to standard GBLUP methods. Prediction accuracy for methane emissions increased from 0.09 to 0.30 in train-test validation and from 0.15 to 0.27 in five-fold cross-validation using PCA components explaining 25% of total RMC variation. …”
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Retentive neural quantum states: efficient ansätze for ab initio quantum chemistry by Oliver Knitter, Dan Zhao, James Stokes, Martin Ganahl, Stefan Leichenauer, Shravan Veerapaneni

“…Neural-network quantum states (NQS) has emerged as a powerful application of quantum-inspired deep learning for variational Monte Carlo methods, offering a competitive alternative to existing techniques for identifying ground states of quantum problems. …”
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Design of an integrated model using deep reinforcement learning and Variational Autoencoders for enhanced quantum security by Harshala Shingne, Diptee Chikmurge, Priya Parkhi, Poorva Agrawal

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Vegetation Quality Assessment of the Shaanxi Section of the Yellow River Basin Based on NDVI and Rain-Use Efficiency by Zhao Liu, Danyue Wang, Lei Han, Hongliang Kang, Xinxin Cao

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Controlling Mie scattering response to refractive index variations via light field manipulation by Jianzhi Zeng, Hanqing Cai, Jiachen Liu, Haifeng Hu

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Adaptive Total Variation Minimization-Based Image Enhancement from Flash and No-Flash Pairs by Sang Min Yoon, Yeon Ju Lee, Gang-Joon Yoon, Jungho Yoon

“…In this approach, we propose a method based on Adaptive Total Variation Minimization (ATVM) so that it has an efficient image denoising effect by preserving strong gradients of the flash image. …”
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Analyzing the Impact of Climate Change on Compound Flooding Under Interdecadal Variations in Rainfall and Tide by Jiun-Huei Jang, Tien-Hao Chang, Yen-Mo Wu, Ting-En Liao, Chih-Hung Hsu

“…However, relevant research has been limited because significant amounts of data, scenarios, and computations are often required to evaluate long-term variations in compound flood risk. In this study, a framework was proposed through efficient hydraulic simulations and a consequence-based statistical method using data projected under different general circulation models (GCMs). …”
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Testing life-cycle assessment data quality with Benford’s law reveals geographic variation by Bogdan Šinik, Aleksandar Tošić

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Comprehensive response assessment of water use efficiency to climate change in ecologically fragile karst areas by Baiyu Lu, Hang Yao, Linsen Zhu, Zhili Chen

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A Systematic Review of the Advances and New Insights into Copy Number Variations in Plant Genomes by Saimire Silaiyiman, Jiaxuan Liu, Jiaxin Wu, Lejun Ouyang, Zheng Cao, Chao Shen

“…Copy number variations (CNVs), as an important structural variant in genomes, are widely present in plants, affecting their phenotype and adaptability. …”
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Daily Runoff Prediction Model Based on Multivariate Variational Mode Decomposition and Correlation Reconstruction by DING Jie, TU Peng-fei, FENG Yu, ZENG Huai-en

“…The original data included daily runoff from January 2005 to December 2012. [Methods] This study first employed Multivariate Variational Mode Decomposition(MVMD) to decompose the original daily runoff data from the two stations, reducing data complexity. …”
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An algorithm for variational inclusion problems including quasi-nonexpansive mappings with applications in osteoporosis prediction by Raweerote Suparatulatorn, Wongthawat Liawrungrueang, Thanasak Mouktonglang, Watcharaporn Cholamjiak

“…This paper has proposed a novel algorithm for solving fixed point problems for quasi-nonexpansive mappings and variational inclusion problems within a real Hilbert space. …”
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Experimental Study on Variation Rules of Damping with Influential Factors of Tuned Liquid Column Damper by Yang Yu, Lixin Xu, Liang Zhang

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Exploring the Trade-Off in the Variational Information Bottleneck for Regression with a Single Training Run by Sota Kudo, Naoaki Ono, Shigehiko Kanaya, Ming Huang

“…This study analyzes the Variational Information Bottleneck (VIB), a standard IB method in deep learning, in the settings of regression problems and derives its optimal solution. …”
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