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Securing the Private Key in Your Blockchain Wallet: A Continuous Authentication Approach Based on Behavioral Biometric

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Journal of Physics: Conference Series 2020年第1期1631卷

作者： Teng Hu Xiaolei Liu Weina Niu Kangyi Ding Yanping Wang Xiaosong Zhang Institute for Cyber Security School of Computer Science and Engineering University of Electronic Science and Technology of China China Institute of Computer Application China Academy of Engineering Physics China Peng Cheng Laboratory Cyberspace Security Research Center China

As blockchain is increasingly valued, the application of blockchain and its security has become a research hotspot. Not only that, more and more ordinary users are paying attention to the blockchain and beginning to trade commodities in cryptocurrencies. The blockchain wallet is a program for users to manage and stores their private keys. Furthermore, the private key is the user's unique credential to the cryptocurrency in the blockchain. But for now, blockchain wallets are only using passwords to authenticate users, which poses a potential risk to private key security. To mitigate this risk, this paper proposes a continuous authentication approach based on user mouse behavioral biometrics. The approach was experimentally shown to be able to authenticate once per second with an average FAR and FRR of 6.92% and 6.18%, respectively.

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application of robust hybrid tree-based machine learning methods in accurate prediction of underground rock saturation exponent

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Measurement: Journal of the International Measurement Confederation 2025年 255卷

作者： Adhab, Ayat Hussein Bhogayata, Ankur Yadav, Anupam Meena, Y.R. Kalia, Rishiv PRIYADHARSHINI, B. Bisht, Manish Kumar Mahdi, Morug Salih Mansoor, Aseel Salah Radi, Usama Kadem Abd, Nasr Saadoun Mottaghi, Mehrdad Department of Pharmacy Al-Zahrawi University College Karbala Iraq Marwadi University Research Center Department of Civil Engineering Faculty of Engineering & Technology Marwadi University Gujarat Rajkot360003 India Department of Computer Engineering and Application GLA University Mathura281406 India Karnataka Bangalore India Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India Department of Civil Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai India School of Management Graphic Era Hill University Haldwani India Graphic Era Deemed to be University Uttarakhand Dehradun248002 India College of MLT Ahl Al Bayt University Iraq Gilgamesh Ahliya University Baghdad Iraq Collage of Pharmacy National University of Science and Technology Dhi Qar64001 Iraq Medical Technical College Al-Farahidi University Iraq Faculty of Chemistry Kabul University Kabul Afghanistan

The accurate estimation of the saturation exponent is critical for the reliable evaluation of hydrocarbon reserves in petroleum-bearing formations. Traditional laboratory-based measurements for this parameter are both costly and time-consuming. To address this issue, this study introduces a robust data-driven framework employing hybrid tree-based machine learning algorithms, namely Random Forest, Gradient Boosting Machine, Categorical Boosting (CatBoost), Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), Extra Trees, and Decision Tree, to predict the rock saturation exponent using five input features: porosity, permeability, true resistivity, resistivity index, and water saturation. A total of 943 real field data points from Iraqi oil and gas fields were used. Model hyperparameters were optimized using Tree-structured Parzen Estimator (TPE) combined with 5-fold cross-validation to improve generalization and minimize overfitting. The results demonstrated that Gradient Boosting Machine and Random Forest achieved the highest predictive precision with R2 values of 0.89 and 0.83 on the test set, and AARE% of 8.25% and 8.31%, respectively. Conversely, the Decision Tree algorithm yielded the lowest performance. Notably, this study presents a novel application of a comparative and systematically optimized ensemble modeling strategy for saturation exponent prediction, filling a research gap in the robust, interpretable, and accurate modeling of this complex petrophysical parameter. This research advances previous efforts by integrating outlier detection, sensitivity analysis, and optimization-based hyperparameter tuning, resulting in a practical and efficient workflow applicable to diverse geological datasets. © 2025 Elsevier Ltd

关键词： Oil bearing formations

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Deep learning based segmentation of various brain lesions for radiosurgery

arXiv

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arXiv 2020年

作者： Wu, Siang-Ruei Chang, Hao-Yun Su, Florence T. Liao, Heng-Chun Tseng, Wanju Liao, Chun-Chih Lai, Feipei Hsu, Feng-Ming Xiao, Furen Department of Electrical Engineering National Taiwan University College of Arts and Sciences Santa Clara University School of Medicine National Taiwan University Taiwan Data Intelligence and Application Division QNAP Systems Inc Department of Neurosurgery Taipei Hospital Department of Computer Science and Information Engineering National Taiwan University Taiwan Department of Oncology National Taiwan University Hospital Taiwan Department of Neurosurgery National Taiwan University Hospital Taiwan

Semantic segmentation of medical images with deep learning models is rapidly developed. In this study, we benchmarked state-of-the-art deep learning segmentation algorithms on our clinical stereotactic radiosurgery dataset, demonstrating the strengths and weaknesses of these algorithms in a fairly practical scenario. In particular, we compared the model performances with respect to their sampling method, model architecture, and the choice of loss functions, identifying the suitable settings for their applications and shedding light on the possible improvements. Copyright © 2020, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Cross-ethnicity face anti-spoofing recognition challenge: A review

arXiv

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arXiv 2020年

作者： Liu, Ajian Li, Xuan Wan, Jun Liang, Yanyan Escalera, Sergio Escalante, Hugo Jair Madadi, Meysam Jin, Yi Wu, Zhuoyuan Yu, Xiaogang Tan, Zichang Yuan, Qi Yang, Ruikun Zhou, Benjia Guo, Guodong Li, Stan Z. Faculty of Information Technology Avenida WaiLong Taipa Macau China School of Computer and Information Technology Beijing Jiaotong University Beijing China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Science Beijing China Universitat de Barcelona and Computer Vision Center Barcelona Instituto Nacional de Astrofísica Óptica y Electrónica Puebla Mexico School of Software Beihang University Beijing China Institute of Deep Learning Baidu Research and National Engineering Laboratory for Deep Learning Technology and Application Beijing Westlake University Hangzhou China

Face anti-spoofing is critical to prevent face recognition systems from a security breach. The biometrics community has achieved impressive progress recently due the excellent performance of deep neural networks and the availability of large datasets. Although ethnic bias has been verified to severely affect the performance of face recognition systems, it still remains an open research problem in face anti-spoofing. Recently, a multi-ethnic face anti-spoofing dataset, CASIA-SURF CeFA, has been released with the goal of measuring the ethnic bias. It is the largest up to date cross-ethnicity face anti-spoofing dataset covering 3 ethnicities, 3 modalities, 1;607 subjects, 2D plus 3D attack types, and the first dataset including explicit ethnic labels among the recently released datasets for face *** organized the Chalearn Face Anti-spoofing Attack Detection Challenge which consists of single-modal (e.g., RGB) and multi-modal (e.g., RGB, Depth, Infrared (IR)) tracks around this novel resource to boost research aiming to alleviate the ethnic bias. Both tracks have attracted 340 teams in the development stage, and finally 11 and 8 teams have submitted their codes in the single-modal and multi-modal face anti-spoofing recognition challenges, respectively. All the results were verified and re-ran by the organizing team, and the results were used for the final ranking. This paper presents an overview of the challenge, including its design, evaluation protocol and a summary of results. We analyze the top ranked solutions and draw conclusions derived from the competition. In addition we outline future work directions. Copyright © 2020, The Authors. All rights reserved.

关键词： Large dataset

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Cross-cluster asymmetric group key agreement for wireless sensor networks

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science China(Information sciences) 2018年第4期61卷 226-228页

作者： Jiamin ZHENG Yu'an TAN Qikun ZHANG Xiaosong ZHANG Liehuang ZHU Quanxin ZHANG School of Computer Science and Technology Beijing Institute of Technology Research Center of Massive Language Information Processing and Cloud Computing Application Institute of Computer and Communication Engineering Zhengzhou University of Light Industry Department of Computer Science and Technology Tangshan University

Wireless sensor networks have some obvious negative characteristics,such as limited communication range,energy constraints,and vulnerability.A group key agreement in this environment requires lightweight cross-cluster... 详细信息

关键词： Cross-cluster asymmetric group key agreement for wireless sensor networks

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Evaluating Local Geometric Feature Representations for 3D Rigid Data Matching

arXiv

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arXiv 2019年

作者： Yang, Jiaqi Quan, Siwen Wang, Peng Zhang, Yanning National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science Northwestern Poly-technical University Xi’an710129 China School of Electronic and Control Engineering Chang’An University Xi’an710064 China

—Local geometric descriptors remain an essential component for 3D rigid data matching and fusion. The devise of a rotational invariant local geometric descriptor usually consists of two steps: local reference frame (LRF) construction and feature representation. Existing evaluation efforts have mainly been paid on the LRF or the overall descriptor, yet the quantitative comparison of feature representations remains unexplored. This paper fills this gap by comprehensively evaluating nine state-of-the-art local geometric feature representations. Our evaluation is on the ground that ground-truth LRFs are leveraged such that the ranking of tested feature representations are more convincing as opposed to existing studies. The experiments are deployed on six standard datasets with various application scenarios (shape retrieval, point cloud registration, and object recognition) and data modalities (LiDAR, Kinect, and Space Time) as well as perturbations including Gaussian noise, shot noise, data decimation, clutter, occlusion, and limited overlap. The evaluated terms cover the major concerns for a feature representation, e.g., distinctiveness, robustness, compactness, and efficiency. The outcomes present interesting findings that may shed new light on this community and provide complementary perspectives to existing evaluations on the topic of local geometric feature description. A summary of evaluated methods regarding their peculiarities is also presented to guide real-world applications and new descriptor crafting. Copyright © 2019, The Authors. All rights reserved.

关键词： Shot noise

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Three-dimensional spin Hall effect of light in tight focusing

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Physical Review A 2020年第2期101卷 023819-023819页

作者： Weixing Shu Chuyang Lin Jun Wu Shizhen Chen Xiaohui Ling Xinxing Zhou Hailu Luo Shuangchun Wen Key Laboratory for Micro/Nano Optoelectronic Devices of Ministry of Education & Hunan Provincial Key Laboratory of Low-Dimensional Structural Physics and Devices School of Physics and Electronics Hunan University Changsha 410082 China Key Laboratory of Low Dimensional Quantum Structures and Quantum Control of Ministry of Education Hunan Normal University Changsha 410081 China College of Computer Science and Electronic Engineering Hunan University Changsha 410082 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application College of Physics and Electronic Engineering Hengyang Normal University Hengyang 421002 China

We theoretically propose the realization of a three-dimensional (3D) spin Hall effect (SHE) of light by tightly focusing a linearly polarized light. Owing to the spin-orbital interaction in focusing, the left and right circularly polarized components of light are partly converted into each other, acquiring opposite vortex phases. As the superposition of the vortex and the initial vortex-free light, the two polarized components exhibit spiral intensity patterns which rotate oppositely around the propagation axis. Consequently, the two spin components are distributed alternatively in both the azimuthal and the longitudinal directions, leading to a 3D array of split spin components, and the transverse intensity is also spit asynchronously. Moreover, that array is rotatable by the incident polarization, and the longitudinal spacing in the array can be controlled by the numerical aperture of the lens. The 3D SHE of light finds an additional degree of freedom available to control the spin state of the photon, which may be valuable in optical manipulation and quantum information.

关键词： Classical optics

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Validation of right coronary artery lumen area from cardiac computed tomography against intravascular ultrasound

Validation of right coronary artery lumen area from cardiac ...

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作者： Cui, Hengfei Xia, Yong Zhang, Yanning Zhong, Liang National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an710072 China School of Computer Science and Engineering Northwestern Polytechnical University Xi’an710072 China National Heart Centre Singapore 5 Hospital Drive Singapore169609 Singapore

Quantification of coronary artery disease (CAD) from cardiac computed tomography angiography (CTA) is important both structurally (lumen area stenosis) and functionally (combined with computational fluid dynamics to determine fractional flow reserve) for assessment of ischemic stenosis and to guide treatment. Hence, it is important to have CTA image processing technique for segmentation and reconstruction of coronary arteries. In this study, we developed segmentation and reconstruction techniques, based on fast marching and Runge–Kutta methods for centerline extraction, and surface mesh generation. The accuracy of the reconstructed models was validated with direct intravascular ultrasound (IVUS) measurements in 1950 cross sections within 4 arteries. High correlation was found between CTA and IVUS measurements for lumen areas (r= 0.993 , p2, 6 mm 2, 7 mm 2 and 8 mm 2, all ROC values >0.99). We conclude that our technique has sufficient accuracy for quantifying coronary lumen area. The accuracy and efficiency demonstrated that our approach can facilitate quantitative evaluation of coronary stenosis and potentially help in real-time assessment of CAD. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Runge Kutta methods

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Nonlinear Cross-Domain Feature Representation Learning Method Based on Dual Constraints

Nonlinear Cross-Domain Feature Representation Learning Metho...

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IEEE International Conference on Big Knowledge (ICBK)

作者： Han Ding Yuhong Zhang Shuai Yang Yaojin Lin Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Data Science and Intelligence Application Fujian Province University Zhangzhou China

Feature representation learning is a research focus in domain adaptation. Recently, due to the fast training speed, the marginalized Denoising Autoencoder (mDA) as a standing deep learning model has been widely utilized for feature representation learning. However, the training of mDA suffers from the lack of nonlinear relationship and does not explicitly consider the distribution discrepancy between domains. To address these problems, this paper proposes a novel method for feature representation learning, namely Nonlinear cross-domain Feature learning based Dual Constraints (NFDC), which consists of kernelization and dual constraints. Firstly, we introduce kernelization to effectively extract nonlinear relationship in feature representation learning. Secondly, we design dual constraints including Maximum Mean Discrepancy (MMD) and Manifold Regularization (MR) in order to minimize distribution discrepancy during the training process. Experimental results show that our approach is superior to several state-of-the-art methods in domain adaptation tasks.

关键词： Noise reduction Manifolds Training Adaptation models Kernel Deep learning Feature extraction

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A Study on the Differences of Attitude Learning and Learning Behavior Sequences for University computer MOOC+ SPOC Course

A Study on the Differences of Attitude Learning and Learning...

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International Conference on computer science & Education (ICCSE)

作者： Zhao Changhua Mi Chunqiao School of Computer Science and Engineering Huaihua University Huaihua P. R. China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Huaihua P. R. China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua P. R. China

ISBN: (数字)9781728172675

ISBN: (纸本)9781728172682

The outbreak of COVID-19 make University computer MOOC+SPOC course face opportunities and challenges. There are some problems in MOOC+SPOC course teaching, such as learners' curiosity decreasing, lack of accomplishment and etc. In order to deal with these problems, this study explores the correlation and differences in the learning behavior sequences of learners with different attitudes to high happiness, medium happiness, and low happiness. Through the learners' comments on University computer MOOC+SPOC course, the learner's attitude information is tapped. By analyzing learning behavior sequences, this study finds out the degree of learning behavior change of learners with different attitudes. The result of this study can improve the existing University computer MOOC+SPOC instructional design and promote the learning mode to move towards intelligent personalized adaptive learning.

关键词： Interviews Electronic learning Data analysis Encoding Correlation

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